Fine-grained visual categorization (FGVC) dealt with objects belonging to one class with intra-class differences into subclasses. FGVC is challenging due to the fact that it is very difficult to collect enough training samples. This study presents a novel image dataset named Cowbreefor FGVC. Cowbree dataset contains 4000 images belongs to eight different cow breeds. Images are properly categorized under different breed names (labels) based on different texture and color features with the help of experts. While evidence shows that the existing dataset are of low quality, targeting few breeds with less number of images. To validate the dataset, three state of the art classifiers sequential minimal optimization (SMO), Multiclass classifier and J48 were used. Their results in term of accuracy are 68.81%, 55.81% and 57.45% respectively. Where results shows that SMO out performed with 68.81% accuracy, 68.4% precision and 68.8% recall.
Vetreckon - India's first digital magazine on Veterinary EducationIbne Ali
The newsletter discusses topics related to veterinary science in India, including transforming veterinary education with an emphasis on practical skills development for students. It also profiles the Murrah buffalo breed which is important for milk production in Punjab, and provides details on its physical characteristics, production traits, and population. Finally, it examines the life cycle and prevention of Toxocara vitulorum, a parasitic roundworm that commonly infects cattle calves.
The study was carried out to recognize the domesticated species belonging to the family Bovidae by their specific
macro-microscopic features of dorsal guard hair characteristics. Nowadays the domesticated animals played a vital role in the
dairy industry and in providing easy prey-base for the various top predators which found to occur throughout the Gujarat area
including protected and non-protected areas. In this, we collected control hair samples from the various cattle owners distributed
in whole Saurashtra region of the Gujarat State in the year 2018. The total randomly picked up one hundred twenty guard hairs
from a dorsal region of the four different cattle species were analyzed under microscopes to avail authenticated and the
photographic evidence for the further carnivore scat analysis through this key. In this study, we used the recognizable qualitative
and quantitative features of cuticle as well as medulla of the hair. Medullary Index (MI) found higher in domesticated Sheep
0.93±0.01, followed by Water Buffalo 0.9±0.02, followed by domesticated Goat 0.77±0.01, which further followed by
domesticated cow 0.5±0.10 μm with lower MI. In this study we used the identifiable qualitative and quantitative features of
Cuticle as well as Medulla.
This document discusses goat farming as a tool for poverty reduction in Bangladesh. It notes that goat farming can provide sustainable livelihoods and income for small farmers and the rural poor. Goat rearing is particularly suitable given the short generation time of goats, their high prolificacy and market value. The document then discusses the characteristics and challenges of goat farming in Bangladesh, focusing on the native Black Bengal breed. It notes that collective action through goat clusters can help farmers access services and markets. The conclusion is that goat farming has significant potential to improve food security and livelihoods in rural Bangladesh if supported by improved practices, infrastructure, and policy support.
An Intelligent system for identification of Indian Lentil types using Artific...IOSR Journals
Abstract: Lentils are designated into two classes, Red Lentils and Lentils other than red. The method of
determining the class of a lentil is by seed coat color. Red lentils may be confirmed by the cotyledon color.
Lentil varieties may have a wide range of seed coat colors from green, red, speckled green, black and tan. The
cotyledon color may be red, yellow or green. The size and color of each Indian Lentil type (i.e. Red, Green and
Yellow) is determined to be large or Medium or small, then size and color becomes part of the grade name. An
Intelligent system is used to identify the type of Indian lentils from bulk samples. The samples were acquired
from the proposed image acquisition system with image resolution 640x480. The proposed system facilitating
kernels size and color measurement using image processing techniques. These Lentil size measurements, when
combined with color attributes of the sample, classify three lentil varieties commonly grown in India with an
accuracy approaching 97.08%.
Key Words: Size and Color measurement, Lentil, Image Processing, Artificial Neural Network
Reviving the Indigenous Poultry Breed - Kadaknath - Enhancing Livelihoods of ...copppldsecretariat
This Note showcases a government initiative to promote and introduce in new areas, indigenous poultry - Kadaknath, in order to enable bio-diversity conservation as well as enhance livelihoods that can reap benefits for the tribal poultry rearers as well as maintain their poultry heritage.
Although the project is fairly recent and support of the implementing agency is presently continuing, a number of lessons emerging from the practice can help future replication thereby establishing it as a sustainable community initiative.
[ Originally posted on http://www.cop-ppld.net/cop_knowledge_base ]
This document is a curriculum vitae for Dr. Ayotunde O. Adebambo, who works as a research scientist and lecturer at the Federal University of Agriculture in Nigeria. His research focuses on improving indigenous livestock breeds in Nigeria through quantitative trait analysis, population characterization, and association studies. He has published several papers on developing improved broiler chicken lines through genetic analysis and molecular characterization of indigenous poultry, goat, and sheep breeds. Dr. Adebambo has also participated in various training programs to further his skills in genome-wide analysis, association studies, and gene expression research.
Role of Poultry in alleviating the poverty and malnutrition in IndiaBalaraj BL
This document summarizes information from a seminar on major credit and poverty in India. It discusses poverty levels among social groups and occupations. It also provides statistics on malnutrition, agriculture contribution to GDP, land holding patterns, poultry production statistics, and the role of the poultry sector in nutrition, poverty reduction, and employment. Challenges for small scale poultry farmers are also outlined.
Community Breeding Practice and the Challenges in Dairy Cattle Management in ...Agriculture Journal IJOEAR
Abstract— Dairy product is an important feed supplementation. However, rearing dairy cattle is became a challenge especially at smallholder level due to lack of indigenous dairy cattle breeds. As a result, the country has established cross breeding program since 1990`s. Nevertheless, it is not uniform and does not consider smallholder farmers. Therefore, this research initiated to identify the breeding practice and the main challenges of smallholder farmers in rearing dairy cattle. For this purpose, first systematic field survey was conducted to generate preliminary information and breed judgment. Then two study areas were selected purposively based on milk production potential and dairy breed characteristics. Two focus group discussions per study area were conducted. Then, randomly 200 respondents per study area were selected. Finally, individual interview and field observation were employed to generate the needed data. Even though there is no established breeding scheme, the smallholder farmers practiced breed improvement by either cross breeding or straight breeding. They implemented Cross breeding to improve milk productivity using 50-100% exotic blood sire/semen. Pure exotic semens were preferred for AI (Artificial insemination). The smallholder framers do not select indigenous caw for cross breeding practice. However, they selected indigenous caw/heifer for replacement based on pedigree history, body stature, and udder vein. Regarding mating system, more than 50% of the respondent exercise control-mating system in their herd. Mostly this was done by pertaining the sire and dam together for a single day. In addition to this partiality in preference of sire, less productive individual cattle culled from the herd by castration, sell and slaughtering. The management system, early castration and lack of accurate estrus detection were the major challenges in dairy cattle development. As the result of this survey in the two study areas, indiscriminate cross breeding without consideration of the production system, body size and blood levels were predominant. Therefore, successive training for smallholder farmers on breeding system is highly recommended.
Vetreckon - India's first digital magazine on Veterinary EducationIbne Ali
The newsletter discusses topics related to veterinary science in India, including transforming veterinary education with an emphasis on practical skills development for students. It also profiles the Murrah buffalo breed which is important for milk production in Punjab, and provides details on its physical characteristics, production traits, and population. Finally, it examines the life cycle and prevention of Toxocara vitulorum, a parasitic roundworm that commonly infects cattle calves.
The study was carried out to recognize the domesticated species belonging to the family Bovidae by their specific
macro-microscopic features of dorsal guard hair characteristics. Nowadays the domesticated animals played a vital role in the
dairy industry and in providing easy prey-base for the various top predators which found to occur throughout the Gujarat area
including protected and non-protected areas. In this, we collected control hair samples from the various cattle owners distributed
in whole Saurashtra region of the Gujarat State in the year 2018. The total randomly picked up one hundred twenty guard hairs
from a dorsal region of the four different cattle species were analyzed under microscopes to avail authenticated and the
photographic evidence for the further carnivore scat analysis through this key. In this study, we used the recognizable qualitative
and quantitative features of cuticle as well as medulla of the hair. Medullary Index (MI) found higher in domesticated Sheep
0.93±0.01, followed by Water Buffalo 0.9±0.02, followed by domesticated Goat 0.77±0.01, which further followed by
domesticated cow 0.5±0.10 μm with lower MI. In this study we used the identifiable qualitative and quantitative features of
Cuticle as well as Medulla.
This document discusses goat farming as a tool for poverty reduction in Bangladesh. It notes that goat farming can provide sustainable livelihoods and income for small farmers and the rural poor. Goat rearing is particularly suitable given the short generation time of goats, their high prolificacy and market value. The document then discusses the characteristics and challenges of goat farming in Bangladesh, focusing on the native Black Bengal breed. It notes that collective action through goat clusters can help farmers access services and markets. The conclusion is that goat farming has significant potential to improve food security and livelihoods in rural Bangladesh if supported by improved practices, infrastructure, and policy support.
An Intelligent system for identification of Indian Lentil types using Artific...IOSR Journals
Abstract: Lentils are designated into two classes, Red Lentils and Lentils other than red. The method of
determining the class of a lentil is by seed coat color. Red lentils may be confirmed by the cotyledon color.
Lentil varieties may have a wide range of seed coat colors from green, red, speckled green, black and tan. The
cotyledon color may be red, yellow or green. The size and color of each Indian Lentil type (i.e. Red, Green and
Yellow) is determined to be large or Medium or small, then size and color becomes part of the grade name. An
Intelligent system is used to identify the type of Indian lentils from bulk samples. The samples were acquired
from the proposed image acquisition system with image resolution 640x480. The proposed system facilitating
kernels size and color measurement using image processing techniques. These Lentil size measurements, when
combined with color attributes of the sample, classify three lentil varieties commonly grown in India with an
accuracy approaching 97.08%.
Key Words: Size and Color measurement, Lentil, Image Processing, Artificial Neural Network
Reviving the Indigenous Poultry Breed - Kadaknath - Enhancing Livelihoods of ...copppldsecretariat
This Note showcases a government initiative to promote and introduce in new areas, indigenous poultry - Kadaknath, in order to enable bio-diversity conservation as well as enhance livelihoods that can reap benefits for the tribal poultry rearers as well as maintain their poultry heritage.
Although the project is fairly recent and support of the implementing agency is presently continuing, a number of lessons emerging from the practice can help future replication thereby establishing it as a sustainable community initiative.
[ Originally posted on http://www.cop-ppld.net/cop_knowledge_base ]
This document is a curriculum vitae for Dr. Ayotunde O. Adebambo, who works as a research scientist and lecturer at the Federal University of Agriculture in Nigeria. His research focuses on improving indigenous livestock breeds in Nigeria through quantitative trait analysis, population characterization, and association studies. He has published several papers on developing improved broiler chicken lines through genetic analysis and molecular characterization of indigenous poultry, goat, and sheep breeds. Dr. Adebambo has also participated in various training programs to further his skills in genome-wide analysis, association studies, and gene expression research.
Role of Poultry in alleviating the poverty and malnutrition in IndiaBalaraj BL
This document summarizes information from a seminar on major credit and poverty in India. It discusses poverty levels among social groups and occupations. It also provides statistics on malnutrition, agriculture contribution to GDP, land holding patterns, poultry production statistics, and the role of the poultry sector in nutrition, poverty reduction, and employment. Challenges for small scale poultry farmers are also outlined.
Community Breeding Practice and the Challenges in Dairy Cattle Management in ...Agriculture Journal IJOEAR
Abstract— Dairy product is an important feed supplementation. However, rearing dairy cattle is became a challenge especially at smallholder level due to lack of indigenous dairy cattle breeds. As a result, the country has established cross breeding program since 1990`s. Nevertheless, it is not uniform and does not consider smallholder farmers. Therefore, this research initiated to identify the breeding practice and the main challenges of smallholder farmers in rearing dairy cattle. For this purpose, first systematic field survey was conducted to generate preliminary information and breed judgment. Then two study areas were selected purposively based on milk production potential and dairy breed characteristics. Two focus group discussions per study area were conducted. Then, randomly 200 respondents per study area were selected. Finally, individual interview and field observation were employed to generate the needed data. Even though there is no established breeding scheme, the smallholder farmers practiced breed improvement by either cross breeding or straight breeding. They implemented Cross breeding to improve milk productivity using 50-100% exotic blood sire/semen. Pure exotic semens were preferred for AI (Artificial insemination). The smallholder framers do not select indigenous caw for cross breeding practice. However, they selected indigenous caw/heifer for replacement based on pedigree history, body stature, and udder vein. Regarding mating system, more than 50% of the respondent exercise control-mating system in their herd. Mostly this was done by pertaining the sire and dam together for a single day. In addition to this partiality in preference of sire, less productive individual cattle culled from the herd by castration, sell and slaughtering. The management system, early castration and lack of accurate estrus detection were the major challenges in dairy cattle development. As the result of this survey in the two study areas, indiscriminate cross breeding without consideration of the production system, body size and blood levels were predominant. Therefore, successive training for smallholder farmers on breeding system is highly recommended.
This document provides a case study on emu farming conducted in Anand district of Gujarat state, India. It discusses the objectives of studying emu farming, products and uses. It describes the requirements and costs for establishing an emu farm with a minimum of 10 pairs of emus. Key points include:
- Emu farming is an emerging industry that diversifies poultry farming.
- The case study focuses on a large emu farm in Anand district owned by Mr. Mahesh Patel.
- Products from emus include eggs, chicks, oil, meat, feathers, skin and nails.
- Establishing a farm for 10 pairs requires land, fencing, shelter,
A platform for testing, delivering, and continuously improving tropically-ada...ILRI
Presented by Tadelle Dessie at the Technology for African Agricultural Transformation (TAAT) Small Ruminants Value Chain Inception Meeting, ILRI, Addis Ababa, 22 June 2018
The document discusses several aspects of livestock farming, including the instinct and intelligence of farm animals, the development of intensive farming methods, selective breeding, free-range farming, food production efficiency, and campaigns regarding intensive farming practices. It addresses how farming techniques have intensified over time through selective breeding and confinement, as well as alternatives like free-range systems. The summary touches on debates around intensive versus extensive farming and choices for consumers.
The document provides an overview of the cattle feed industry globally and in India. It discusses that the cattle feed industry in India started around 1965 to cater to the dairy industry. The industry utilizes indigenous raw materials like coconut cake as a key ingredient. The global cattle feed market is growing due to rising demand for meat and meat products. Major markets include Europe, US and emerging markets in Asia. The industry relies on grain, soy and fishmeal which are imported into many developing countries. Intensive livestock production has increased efficiency but also relies on external inputs at large economies of scale.
Ahmed usman Research proposal final ppts.pptxAdemMustefa
- The document discusses a proposed MSc research project to identify breeding objective traits for the Somali goat breed in Ethiopia's Sitti zone.
- The project will use participatory methods like surveys, interviews, and animal rankings with goat owners to determine important traits like productivity, health, and market preferences.
- Data on traits, coat colors, weights and measurements will be collected from 600 goats and analyzed to identify key breeding objectives to improve the breed and support goat farming in the region.
India ranks first in milk production globally, producing over 32 million liters per day. Gujarat has an annual milk production of 248 million liters per day, with a per capita availability of 435 grams per day. Milk and milk products play a vital role in India's economy, contributing over $105 billion annually. India's dairy industry is dominated by buffaloes, which produce 55% of the country's milk, followed by cows at 40%. The top five milk producing states are Uttar Pradesh, Rajasthan, Andhra Pradesh, Punjab, and Gujarat.
Assessment of productive and reproductive performances of crossAlexander Decker
This document summarizes a study that assessed the productive and reproductive performances of crossbreed dairy cows in Debre Tabor town, Ethiopia. Data was collected through interviews with owners of 5 smallholder farms with crossbreed dairy cows. Key findings include:
1) The average milk production was 9.91 liters per day, with 12 liters, 9.75 liters and 8 liters for the 1st, 2nd, and 3rd stages of lactation respectively. The average lactation length was 9 months.
2) The average age at first calving was 29.52 months. 60% of cows had a calving interval between 1-1.3 years.
3) Housing
Assessment of productive and reproductive performances of crossAlexander Decker
1) The study assessed the productive and reproductive performances of crossbreed dairy cows in five smallholder farms in Debre Tabor town, Ethiopia.
2) Productive performances were analyzed based on milk yield at different lactation stages. On average, cows produced 12 liters in the first stage, 9.75 liters in the second stage, and 8 liters in the third stage, with an overall average of 9.91 liters per day.
3) Reproductive performances like age at first calving, calving interval, and age at first service were also assessed. On average, age at first calving was 29.52 months, calving interval was 391.8 days, and
Pig farming has high returns on investment (ROI) ratio. Another benefit is that pork has a tremendous demand; hence, you have a ready market . Pork is famous because of its nutritious value, has high fat, low water content, and enhanced energy value unlike other types of meat.
This is the English version translated from Swahili, for more plz visit https://www.fiverr.com/mariepetiti
This document provides an overview of the topics covered in the course on Animal Genetics and Breeding. The course is divided into two papers, with the first covering biostatistics and principles of genetics and population genetics, and the second focusing on principles of animal breeding for livestock, poultry, pets and wild animals. Key genetic concepts are defined, including qualitative vs. quantitative traits. Economic traits important for improving cattle, buffalo, sheep, goats and poultry are outlined. The objective of the course is to study genetics and apply the principles of selection and breeding to increase the genetic potential and productivity of animal species. References for further reading on relevant topics are also listed.
A study was conducted to determine the productivity and constraints of smallholder rabbit production in the Mberengwa and Zvishavane districts of the Midlands Province in Zimbabwe. 42 households were randomly selected to respond to a standard questionnaire. 79% of the respondents had an average rabbit rearing experience of less than five years. The average clutch size per household was less than 10 mature rabbits. The rabbits were acquired through purchase (88%), gifts (7%), or in exchange for labor. However 76% of the rabbits were sourced from within the local area. Only 17% of the farmers fed commercial feeds. The prevalent diseases were mange (68%), coccidiosis (24%), and foot rot (8%). 23% of the farmers used veterinary drugs and medicines to treat the common diseases, whilst 67% sourced the locally available herbs and plants for the same purpose. Survivability of the rabbits was significantly affected (P<0.001) and strongly correlated (r=0.) with housing system. Rabbit meat was mainly used to generate household income and for home consumption.
This document presents a project plan to reduce goat meat imports in Nepal by 50% by 2012, 75% by 2013, and 100% by 2015. It analyzes the current situation of goat farming and meat production in Nepal, outlines objectives and limitations. It projects population growth and estimates future demand for goat meat. The plan involves establishing farmer-managed goat breeding centers, strengthening existing centers, and supporting commercial farms to increase domestic production and meet demand without imports by 2015. A SWOT analysis identifies strengths like goats' hardiness and opportunities like diverse habitats, as well as weaknesses like lack of funding and threats like disease.
The role of veterinary science in international developmentILRI
Keynote address by Tarni Cooper at the School of Animal and Veterinary Science DVM1 Clinical Research Project and Honours Project Presentation Day, University of Adelaide, Australia, 7 November 2014.
Assessing Artificial Insemination Service Effectiveness and Evaluation of Sem...PriyankaKilaniya
This study was conducted to assess the effectiveness of artificial insemination and semen quality in the Dodola district of the Oromia region of Ethiopia. A cross-sectional survey with structured questionnaires and a stratified sample approach was used to gather data from 264 smallholder dairy households (168 rural and 96 peri-urban households). Furthermore, 32 frozen semen straws were collected using a random sampling approach to assess the quality of the frozen semen based on handling effectiveness. Additionally, the number of services per conception, non-return rate, and conception rate were determined using retrospective data spanning two years (2020–2021). The survey results show that,30.7% of the dairy farmers in the study area regularly and uninterruptedly receive artificial insemination services, while 69.3% do not, citing a lack of inputs, a shortage of artificial insemination technicians, and service interruptions on weekends and holidays. The overall mean numbers of services per conception, non-return rate, and conception rate in the study area were 2.16, 42.9%, and 45%, respectively. Improper management of liquid nitrogen containers, improper semen deposition in the reproductive tract, neglecting basic AI equipment, and improperly dried straw after removal from warm water thawing were the main issues with semen handling in the study area. The average motility and viability of frozen semen from Source: Laboratory result (2022) was 67.3 ± 5.82 and 78.9 ± 5.77, respectively, but in the district, they were 49.9 ± 5.3 and 59.8 ± 7.4, respectively. According to the results of the survey and experiment, the overall success rate of artificial insemination services was unsatisfactory, with conception failure and improper handling of semen being particularly critical issues that need urgent attention. Therefore, it is important to provide artificial insemination technicians with regular training and sensitization to advance their expertise. However, robust structural integration between logistics centers and supply chains for artificial insemination inputs is necessary to optimize the effectiveness of these services.
Assessing Artificial Insemination Service Effectiveness and Evaluation of Sem...PriyankaKilaniya
This study was conducted to assess the effectiveness of artificial insemination and semen quality in the Dodola district of the Oromia region of Ethiopia. A cross-sectional survey with structured questionnaires and a stratified sample approach was used to gather data from 264 smallholder dairy households (168 rural and 96 peri-urban households). Furthermore, 32 frozen semen straws were collected using a random sampling approach to assess the quality of the frozen semen based on handling effectiveness. Additionally, the number of services per conception, non-return rate, and conception rate were determined using retrospective data spanning two years (2020–2021). The survey results show that,30.7% of the dairy farmers in the study area regularly and uninterruptedly receive artificial insemination services, while 69.3% do not, citing a lack of inputs, a shortage of artificial insemination technicians, and service interruptions on weekends and holidays. The overall mean numbers of services per conception, non-return rate, and conception rate in the study area were 2.16, 42.9%, and 45%, respectively. Improper management of liquid nitrogen containers, improper semen deposition in the reproductive tract, neglecting basic AI equipment, and improperly dried straw after removal from warm water thawing were the main issues with semen handling in the study area. The average motility and viability of frozen semen from Source: Laboratory result (2022) was 67.3 ± 5.82 and 78.9 ± 5.77, respectively, but in the district, they were 49.9 ± 5.3 and 59.8 ± 7.4, respectively. According to the results of the survey and experiment, the overall success rate of artificial insemination services was unsatisfactory, with conception failure and improper handling of semen being particularly critical issues that need urgent attention. Therefore, it is important to provide artificial insemination technicians with regular training and sensitization to advance their expertise. However, robust structural integration between logistics centers and supply chains for artificial insemination inputs is necessary to optimize the effectiveness of these services.
Hamster, housing, breeding and management by dr.pavulraj.sPavulraj Selvaraj
This document discusses the production, breeding, and management of laboratory hamsters. It provides details on:
1. Housing requirements for hamsters including appropriate cage sizes, bedding materials, temperature, humidity, and photoperiod.
2. Nutritional requirements of hamsters including suitable diets, feeding methods, and water needs.
3. Breeding of hamsters including sexing, selecting breeding pairs, the estrous cycle, mating/gestation, signs of pregnancy, and caring for pregnant females and newborn pups.
4. General hamster biology such as taxonomy, characteristics, and anatomy.
Comparison In Genetic Population Of Cats At Kampung Ketoyong and Taman IndahANna CHan
This document summarizes a student group's mini project comparing the genetic populations of cats in two areas in Malaysia. The group found 57 cats in Taman Indah and 35 cats in Kampung Ketoyong over two days. They categorized coat colors and found the highest populations were orange in Taman Indah and tabby in Kampung Ketoyong. The group determined that the higher population in Taman Indah was likely due to more available food from shops and waste, while Kampung Ketoyong had fewer cats due to dogs and cultural practices. The project helped identify which location had more cats and factors influencing populations, and showed coat color is controlled by more than just sex linkage.
The document discusses swine production and management. It covers 6 breeds of pigs commonly raised, including their characteristics. It also discusses breeding methods like cross-breeding and upgrading. For farm operations, it describes sow-weaner, farrow-finish, and breeder models. Basic requirements for pigs include proper housing, nutrition, and facilities/equipment. Sows require special care during farrowing and lactation to ensure healthy piglets.
This curriculum vitae summarizes the qualifications and experience of Arvind Kumar. He has over 23 years of experience as a senior scientist and plant breeder, including 12 years working in India and 11 years at the International Rice Research Institute in the Philippines. Some of his key accomplishments include identifying genes and QTLs for biotic and abiotic stress tolerance in rice; developing improved rice varieties with drought, flood, and direct seeding tolerance; and having over 40 rice varieties released in 9 countries that provide 1-1.2 tons/hectare higher yield under drought compared to existing varieties.
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
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Similar to Cowbree: A novel dataset for fine-grained visual categorization
This document provides a case study on emu farming conducted in Anand district of Gujarat state, India. It discusses the objectives of studying emu farming, products and uses. It describes the requirements and costs for establishing an emu farm with a minimum of 10 pairs of emus. Key points include:
- Emu farming is an emerging industry that diversifies poultry farming.
- The case study focuses on a large emu farm in Anand district owned by Mr. Mahesh Patel.
- Products from emus include eggs, chicks, oil, meat, feathers, skin and nails.
- Establishing a farm for 10 pairs requires land, fencing, shelter,
A platform for testing, delivering, and continuously improving tropically-ada...ILRI
Presented by Tadelle Dessie at the Technology for African Agricultural Transformation (TAAT) Small Ruminants Value Chain Inception Meeting, ILRI, Addis Ababa, 22 June 2018
The document discusses several aspects of livestock farming, including the instinct and intelligence of farm animals, the development of intensive farming methods, selective breeding, free-range farming, food production efficiency, and campaigns regarding intensive farming practices. It addresses how farming techniques have intensified over time through selective breeding and confinement, as well as alternatives like free-range systems. The summary touches on debates around intensive versus extensive farming and choices for consumers.
The document provides an overview of the cattle feed industry globally and in India. It discusses that the cattle feed industry in India started around 1965 to cater to the dairy industry. The industry utilizes indigenous raw materials like coconut cake as a key ingredient. The global cattle feed market is growing due to rising demand for meat and meat products. Major markets include Europe, US and emerging markets in Asia. The industry relies on grain, soy and fishmeal which are imported into many developing countries. Intensive livestock production has increased efficiency but also relies on external inputs at large economies of scale.
Ahmed usman Research proposal final ppts.pptxAdemMustefa
- The document discusses a proposed MSc research project to identify breeding objective traits for the Somali goat breed in Ethiopia's Sitti zone.
- The project will use participatory methods like surveys, interviews, and animal rankings with goat owners to determine important traits like productivity, health, and market preferences.
- Data on traits, coat colors, weights and measurements will be collected from 600 goats and analyzed to identify key breeding objectives to improve the breed and support goat farming in the region.
India ranks first in milk production globally, producing over 32 million liters per day. Gujarat has an annual milk production of 248 million liters per day, with a per capita availability of 435 grams per day. Milk and milk products play a vital role in India's economy, contributing over $105 billion annually. India's dairy industry is dominated by buffaloes, which produce 55% of the country's milk, followed by cows at 40%. The top five milk producing states are Uttar Pradesh, Rajasthan, Andhra Pradesh, Punjab, and Gujarat.
Assessment of productive and reproductive performances of crossAlexander Decker
This document summarizes a study that assessed the productive and reproductive performances of crossbreed dairy cows in Debre Tabor town, Ethiopia. Data was collected through interviews with owners of 5 smallholder farms with crossbreed dairy cows. Key findings include:
1) The average milk production was 9.91 liters per day, with 12 liters, 9.75 liters and 8 liters for the 1st, 2nd, and 3rd stages of lactation respectively. The average lactation length was 9 months.
2) The average age at first calving was 29.52 months. 60% of cows had a calving interval between 1-1.3 years.
3) Housing
Assessment of productive and reproductive performances of crossAlexander Decker
1) The study assessed the productive and reproductive performances of crossbreed dairy cows in five smallholder farms in Debre Tabor town, Ethiopia.
2) Productive performances were analyzed based on milk yield at different lactation stages. On average, cows produced 12 liters in the first stage, 9.75 liters in the second stage, and 8 liters in the third stage, with an overall average of 9.91 liters per day.
3) Reproductive performances like age at first calving, calving interval, and age at first service were also assessed. On average, age at first calving was 29.52 months, calving interval was 391.8 days, and
Pig farming has high returns on investment (ROI) ratio. Another benefit is that pork has a tremendous demand; hence, you have a ready market . Pork is famous because of its nutritious value, has high fat, low water content, and enhanced energy value unlike other types of meat.
This is the English version translated from Swahili, for more plz visit https://www.fiverr.com/mariepetiti
This document provides an overview of the topics covered in the course on Animal Genetics and Breeding. The course is divided into two papers, with the first covering biostatistics and principles of genetics and population genetics, and the second focusing on principles of animal breeding for livestock, poultry, pets and wild animals. Key genetic concepts are defined, including qualitative vs. quantitative traits. Economic traits important for improving cattle, buffalo, sheep, goats and poultry are outlined. The objective of the course is to study genetics and apply the principles of selection and breeding to increase the genetic potential and productivity of animal species. References for further reading on relevant topics are also listed.
A study was conducted to determine the productivity and constraints of smallholder rabbit production in the Mberengwa and Zvishavane districts of the Midlands Province in Zimbabwe. 42 households were randomly selected to respond to a standard questionnaire. 79% of the respondents had an average rabbit rearing experience of less than five years. The average clutch size per household was less than 10 mature rabbits. The rabbits were acquired through purchase (88%), gifts (7%), or in exchange for labor. However 76% of the rabbits were sourced from within the local area. Only 17% of the farmers fed commercial feeds. The prevalent diseases were mange (68%), coccidiosis (24%), and foot rot (8%). 23% of the farmers used veterinary drugs and medicines to treat the common diseases, whilst 67% sourced the locally available herbs and plants for the same purpose. Survivability of the rabbits was significantly affected (P<0.001) and strongly correlated (r=0.) with housing system. Rabbit meat was mainly used to generate household income and for home consumption.
This document presents a project plan to reduce goat meat imports in Nepal by 50% by 2012, 75% by 2013, and 100% by 2015. It analyzes the current situation of goat farming and meat production in Nepal, outlines objectives and limitations. It projects population growth and estimates future demand for goat meat. The plan involves establishing farmer-managed goat breeding centers, strengthening existing centers, and supporting commercial farms to increase domestic production and meet demand without imports by 2015. A SWOT analysis identifies strengths like goats' hardiness and opportunities like diverse habitats, as well as weaknesses like lack of funding and threats like disease.
The role of veterinary science in international developmentILRI
Keynote address by Tarni Cooper at the School of Animal and Veterinary Science DVM1 Clinical Research Project and Honours Project Presentation Day, University of Adelaide, Australia, 7 November 2014.
Assessing Artificial Insemination Service Effectiveness and Evaluation of Sem...PriyankaKilaniya
This study was conducted to assess the effectiveness of artificial insemination and semen quality in the Dodola district of the Oromia region of Ethiopia. A cross-sectional survey with structured questionnaires and a stratified sample approach was used to gather data from 264 smallholder dairy households (168 rural and 96 peri-urban households). Furthermore, 32 frozen semen straws were collected using a random sampling approach to assess the quality of the frozen semen based on handling effectiveness. Additionally, the number of services per conception, non-return rate, and conception rate were determined using retrospective data spanning two years (2020–2021). The survey results show that,30.7% of the dairy farmers in the study area regularly and uninterruptedly receive artificial insemination services, while 69.3% do not, citing a lack of inputs, a shortage of artificial insemination technicians, and service interruptions on weekends and holidays. The overall mean numbers of services per conception, non-return rate, and conception rate in the study area were 2.16, 42.9%, and 45%, respectively. Improper management of liquid nitrogen containers, improper semen deposition in the reproductive tract, neglecting basic AI equipment, and improperly dried straw after removal from warm water thawing were the main issues with semen handling in the study area. The average motility and viability of frozen semen from Source: Laboratory result (2022) was 67.3 ± 5.82 and 78.9 ± 5.77, respectively, but in the district, they were 49.9 ± 5.3 and 59.8 ± 7.4, respectively. According to the results of the survey and experiment, the overall success rate of artificial insemination services was unsatisfactory, with conception failure and improper handling of semen being particularly critical issues that need urgent attention. Therefore, it is important to provide artificial insemination technicians with regular training and sensitization to advance their expertise. However, robust structural integration between logistics centers and supply chains for artificial insemination inputs is necessary to optimize the effectiveness of these services.
Assessing Artificial Insemination Service Effectiveness and Evaluation of Sem...PriyankaKilaniya
This study was conducted to assess the effectiveness of artificial insemination and semen quality in the Dodola district of the Oromia region of Ethiopia. A cross-sectional survey with structured questionnaires and a stratified sample approach was used to gather data from 264 smallholder dairy households (168 rural and 96 peri-urban households). Furthermore, 32 frozen semen straws were collected using a random sampling approach to assess the quality of the frozen semen based on handling effectiveness. Additionally, the number of services per conception, non-return rate, and conception rate were determined using retrospective data spanning two years (2020–2021). The survey results show that,30.7% of the dairy farmers in the study area regularly and uninterruptedly receive artificial insemination services, while 69.3% do not, citing a lack of inputs, a shortage of artificial insemination technicians, and service interruptions on weekends and holidays. The overall mean numbers of services per conception, non-return rate, and conception rate in the study area were 2.16, 42.9%, and 45%, respectively. Improper management of liquid nitrogen containers, improper semen deposition in the reproductive tract, neglecting basic AI equipment, and improperly dried straw after removal from warm water thawing were the main issues with semen handling in the study area. The average motility and viability of frozen semen from Source: Laboratory result (2022) was 67.3 ± 5.82 and 78.9 ± 5.77, respectively, but in the district, they were 49.9 ± 5.3 and 59.8 ± 7.4, respectively. According to the results of the survey and experiment, the overall success rate of artificial insemination services was unsatisfactory, with conception failure and improper handling of semen being particularly critical issues that need urgent attention. Therefore, it is important to provide artificial insemination technicians with regular training and sensitization to advance their expertise. However, robust structural integration between logistics centers and supply chains for artificial insemination inputs is necessary to optimize the effectiveness of these services.
Hamster, housing, breeding and management by dr.pavulraj.sPavulraj Selvaraj
This document discusses the production, breeding, and management of laboratory hamsters. It provides details on:
1. Housing requirements for hamsters including appropriate cage sizes, bedding materials, temperature, humidity, and photoperiod.
2. Nutritional requirements of hamsters including suitable diets, feeding methods, and water needs.
3. Breeding of hamsters including sexing, selecting breeding pairs, the estrous cycle, mating/gestation, signs of pregnancy, and caring for pregnant females and newborn pups.
4. General hamster biology such as taxonomy, characteristics, and anatomy.
Comparison In Genetic Population Of Cats At Kampung Ketoyong and Taman IndahANna CHan
This document summarizes a student group's mini project comparing the genetic populations of cats in two areas in Malaysia. The group found 57 cats in Taman Indah and 35 cats in Kampung Ketoyong over two days. They categorized coat colors and found the highest populations were orange in Taman Indah and tabby in Kampung Ketoyong. The group determined that the higher population in Taman Indah was likely due to more available food from shops and waste, while Kampung Ketoyong had fewer cats due to dogs and cultural practices. The project helped identify which location had more cats and factors influencing populations, and showed coat color is controlled by more than just sex linkage.
The document discusses swine production and management. It covers 6 breeds of pigs commonly raised, including their characteristics. It also discusses breeding methods like cross-breeding and upgrading. For farm operations, it describes sow-weaner, farrow-finish, and breeder models. Basic requirements for pigs include proper housing, nutrition, and facilities/equipment. Sows require special care during farrowing and lactation to ensure healthy piglets.
This curriculum vitae summarizes the qualifications and experience of Arvind Kumar. He has over 23 years of experience as a senior scientist and plant breeder, including 12 years working in India and 11 years at the International Rice Research Institute in the Philippines. Some of his key accomplishments include identifying genes and QTLs for biotic and abiotic stress tolerance in rice; developing improved rice varieties with drought, flood, and direct seeding tolerance; and having over 40 rice varieties released in 9 countries that provide 1-1.2 tons/hectare higher yield under drought compared to existing varieties.
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Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
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Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
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A secure and energy saving protocol for wireless sensor networksjournalBEEI
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Plant leaf identification system using convolutional neural networkjournalBEEI
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Customized moodle-based learning management system for socially disadvantaged...journalBEEI
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Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
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Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
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Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
This document describes the implementation of a double-layer structure on an octagon microstrip yagi antenna (OMYA) to improve its performance at 5.8 GHz. The double-layer consists of two double positive (DPS) substrates placed above the OMYA. Simulation and experimental results show that the double-layer configuration increases the gain of the OMYA by 2.5 dB compared to without the double-layer. The measured bandwidth of the OMYA with double-layer is 14.6%, indicating the double-layer can increase both the gain and bandwidth of the OMYA.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
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Transforming data-centric eXtensible markup language into relational database...journalBEEI
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Key performance requirement of future next wireless networks (6G)journalBEEI
The document provides an overview of the key performance indicators (KPIs) for 6G wireless networks compared to 5G networks. Some of the major KPIs discussed for 6G include: achieving data rates of up to 1 Tbps and individual user data rates up to 100 Gbps; reducing latency below 10 milliseconds; supporting up to 10 million connected devices per square kilometer; improving spectral efficiency by up to 100 times through technologies like terahertz communications and smart surfaces; and achieving an energy efficiency of 1 pico-joule per bit transmitted through techniques like wireless power transmission and energy harvesting. The document outlines how 6G aims to integrate terrestrial, aerial and maritime communications into a single network to provide ubiquitous connectivity with higher
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
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An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
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Parking detection system using background subtraction and HSV color segmentationjournalBEEI
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Quality of service performances of video and voice transmission in universal ...journalBEEI
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Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Cowbree: A novel dataset for fine-grained visual categorization
1. Bulletin of Electrical Engineering and Informatics
Vol. 9, No. 5, October 2020, pp. 1882~1889
ISSN: 2302-9285, DOI: 10.11591/eei.v9i5.2443 1882
Journal homepage: http://beei.org
Cowbree: A novel dataset for fine-grained visual categorization
Umar Akbar Khan1
, Saira Moin U. Din2
, Saima Anwar Lashari3
,
Murtaja Ali Saare4
, Muhammad Ilyas5
1,2
Department of Computer Science, the University of Lahore, Sargodha, Pakistan
3
College of Computing and Informatics, Saudi Electronic University, Dammam, Saudi Arabia
4
Universiti Utara Malaysia, Kedah, Malaysia
5
Department of CS &IT, University of Sargodha, Pakistan
Article Info ABSTRACT
Article history:
Received Dec 14, 2019
Revised Feb 22, 2020
Accepted Apr 3, 2020
Fine-grained visual categorization (FGVC) dealt with objects belonging to
one class with intra-class differences into subclasses. FGVC is challenging
due to the fact that it is very difficult to collect enough training samples.
This study presents a novel image dataset named Cowbreefor FGVC.
Cowbree dataset contains 4000 images belongs to eight different cow breeds.
Images are properly categorized under different breed names (labels) based
on different texture and color features with the help of experts. While
evidence shows that the existing dataset are of low quality, targeting few
breeds with less number of images. To validate the dataset, three state of
the art classifiers sequential minimal optimization (SMO), Multiclass
classifier and J48 were used. Their results in term of accuracy are 68.81%,
55.81% and 57.45% respectively. Where results shows that SMO out
performed with 68.81% accuracy, 68.4% precision and 68.8% recall.
Keywords:
Cowbree
Fined-grained visual
categorization
J48
Multiclass
SMO
WEKA image filter package
This is an open access article under the CC BY-SA license.
Corresponding Author:
Saira Moin U. Din,
Department of Computer Science,
The University of Lahore,
10 KM, Lahore Road, Sargodha, Pakistan.
Email: saira.moin@cs.uol.edu.pk
1. INTRODUCTION
Classification task performed without deep knowledge of the domain is always challenging.
It is more difficult when there are specific intra-class differences between objects. In Computer vision
Fine-grained visual categorization (FGVC) refers to categorize the objects belonging to one class but having
the intra-class differences into subclasses. Fine-grained visual categorization (FGVC) is challenging because,
it is often difficult to acquire enough number of training samples [1-2]. The existing datasets in this scenario
have few training samples in each category. Few benchmark datasets like Stanford dogs [3] and Veg Fru [4]
can be considered complete for this scenario, as these datasets have large number of training examples in
each defined category. Collection and classification of data for specific category of object is a very
challenging task. It requires deep domain knowledge and experience. For this particular reason datasets for
FGVC is very limited.
Therefore this study aims to build a novel domain-specific dataset of cow breeds for image fined
grained visual categorization. It is very difficult to differentiate between the different breeds of cow, due to
very minute intra-class differences among them. Like it is very difficult to distinguish Red Sindhi from
Sahiwal cow breed. Both have almost same texture and color features. Only the difference between them
is their head shape. Figure 1 shows the example of Sahiwal and Red Sindhi cow breed.
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Here, it is mandatory to mention that biological categories-species and breeds-have been especially
well-studied, with work tackling subcategory recognition of flowers, dogs, birds, etc. These biological domains,
where taxonomy dictates a clear set of mutually exclusive subcategories, are wonderfully well-suited to the problem,
and recognition systems in these domains are of practical use in ecology and agriculture. In this work
mainly eight famous cow breeds are targeted, the dataset contains 4000 images belonging to eight different
breeds of cow.
Sahiwal
Red
Sindhi
Figure 1. Example of sahiwal and red sindhi
a. Cow breeds
Livestock appears as natural machine for the production of dairy items and meat. It is quite fact that
proteins from animals are much superior to proteins from vegetables. Livestock plays essential part in
economic growth for the countries that mainly depend on agriculture. They provide social security in case of
crop failure or in case of any other natural disaster. Livestock is an important sub-sector of the agriculture
system and responsible for the economic growth of many Asian countries. According to a survey, in 2013
Asian countries produce 39% of world’s cow milk and 97% of world’s buffalo milk [5]. About half of the
world's poor population lives in South Asia and they mainly depend on livestock for their social-economic
growth [6-7]. Only in Pakistan round about 35 million people are engaged with the field of livestock, which
show that livestock is an important and essential part of social system. Livestock involves different types of
animals from a very large size camel to a small size goat but the basic purpose of all types of domestic
animals is the production of meat and milk. Selection of breed is an important factor in this business.
It is much difficult task and needs very deep knowledge. As many cow breeds are much similar in shape
and color, like Red Sindhi and Sahiwal. Red Sindhi and Sahiwal are two different breeds with different milk
production values but in case of color and shape these breeds are quite similar. This problem of breed recognizing
can be solved by technology.
This study focuses on cow breeds. In many countries cow farming is a very large business, selection
of breed is an important factor in this business. It is much difficult task and needs very deep knowledge.
As many cow breeds are much similar in shape and color, like Red Sindhi and Sahiwal. Red Sindhi
and Sahiwal are two different breeds with different milk production values but in case of color and shape
these breeds are quite similar. This problem of breed recognizing can be solved by technology.
Now a day systems are available for automatic detection of animals and birds breed. Like Merlin
Bird Photo ID an android app build by computer vision researchers for automatic detection of round about
400 species of birds in North America. Typically for detection of cow breeds no such type of system
available in market. This study road towards that system; any breed detection system involves two parts one
is building a dataset to train the model; other is the development of model that can be properly trained to
recognize the different classes of objects. Like Zalán Ráduly et al., use benchmark dataset of Stanford dog
breeds for the development of a dog breed identification model [8]. This study basically targets the first part
i.e. building a cow breed dataset. One of the primary objectives of this study is the building of dataset from
natural environment, which contain large number of training examples in each defined category.
For the purpose of data collection field research work is conducted. In field research work we meet
the persons who expert in cow breed identification and research. After discussing the whole scenario with
officials of livestock and dairy development department in Pakistan, we have selected eight cow breeds for
our study. Table 1 shows the short introduction of cow breeds that proposed dataset contains.
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Table 1. Showing the detail of targeted cow breeds
No Bread Image with Name Description
1
Cholistani
Cholistani breed is of medium size, having a brown body with white dots or white body with
small red or brown dots. Their skin is thick and loose and head of animal is medium short, ears
are of medium-sized, horns are usually small and thick; tail of this breed is long.
2
Sahiwal
Sahiwal is a medium-sized breed with fleshy and beautiful body. The body is wedged shaped
with loose skin, animal head is short and horns are small and thick. Hump in male cow is
massive while in female it is normal-sized. Usually male Sahiwal cow have small eyes, broad
forehead, black muzzle, and very loose sheath and dewlap. Tail of this breed is very long
almost touching the ground [9].
3
Red
Sindhi
Red Sindhi is a medium-sized breed with usually dark red skin color. The color of male cow is
much darker at the shoulders. In color this breed has much resemblance with “Sahiwal”, in few
cases it is very difficult to judge the animal that either particular animal belongs to Red Sindhi
or Sahiwal. Horns are thick in male but female of this breed usually has thin horns, ears are fine
small, hump of this breed is well developed.
4
Dhanni
Animals of this breed are medium-sized with different body colors, usually have white skin
with black patches or dots and in some cases animals have white skin with red dots or patches.
The head and ear of the animal of this breed are small, forehead is narrow, and horns are thick
and stumpy. The dewlap is small, back is straight and hump is compact.
5
Bhagnari
Body-color of this breed is usually white or light grey; usually has light black neck color. Head
is of medium size with short strong neck, the breed has black beautiful eyelashes. Usually have
small dewlap and stumpy horns, ears are small and pointed. Straight back and black tuft of the
tail are the prominent features of this breed to recognize it [10].
6
Friesian
Animals of this breed are usually large and very beautiful; their skin color is white with black
or red patches, in some cases their skin color is completely white or black. The straight back of
this breed makes it easy to recognize, the head is long and narrow, the backside of Friesian cow
is very heavy and back legs are straight.
7
Jersey
Jersey breed have usually small body structure, skin color is usually fawn, in some cases it is
Redish brown or dark brown up to black coloring. Head is of long sized and face is narrowed
and pointed, the tail of this breed small and usually in black color [11].
8
Cross-breed
Crossbreeding is a process, in which two breeds are combined to increase efficiency. In this
study where we are collecting images of different breeds of cow from natural environment, we
come to know that this process is very common now a day. Crossbreeds of cattle are more
common than straight breeds due to many reasons, crossbreed animals possess physical
characteristics of two or more breeds this factor makes them very difficult for us to place them
under the label of any straight breed [12].
b. Related work
Saihui Hou et al., in 2018 provide a complete dataset VegFru for FGVC dataset contains 25
categories and more than 100 subcategories of vegetables and fruits. To check the validity of dataset
the experimental work is done by using DCNN model [4]. Grant Van Horn et al in their paper introduce tools
and methodologies to collect high quality, large scale fine-grained computer vision datasets using citizen
scientists. In their research they also described that that learning algorithms based on CNN features and part
localization were surprisingly robust to mislabeled training examples [13].
Similarly Zalan Raduly et al., also contributes to the FGVC problem, they use StanForddogs dataset
to train deep learning models for detection of breed in a given image. They also presents two different
convolutional neural network architectures, the presented architectures have been tested on an image
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classification problem i.e. recognizing dog breeds a fine-grained image recognition problem [8]. Lingyun Li et al.,
use (bird breeds) dataset to check the classification accuracy of fine-tuning algorithms. Lingyun Li et al.,
tackles the issue of over-segmentation that harm the quality of image representation, problem is frequently
observed in the case of unsupervised part detection on fine-grained visual categorization [14]. Shaoyong Yu
et al., in their research present a novel model for fine-grained vehicle classification that based on deep
learning, their work consist of three steps, in first step they generate a dataset of vehicles in second step they use
R-CNN model to detect vehicles in images and in last step they use a CNN model and joint Bayesian network to
classify vehicles in fine-grained [15].
Timnit Gebru et al., proposed a graph-based crowdsourcing algorithm to automatically group
visually not differentiable objects together. Using their workflow, they label 712,430 images that result in
creation of a large fine-grained visual dataset of car models. In 2016 Sun et al., introduced a dataset for
human skin diseases; presented dataset is the largest dataset for visual recognition of skin disease.
Dataset contains 6584 images from 198 classes, for classification purposes CNN and SVM are used in this research
work [16]. Ming Sun et al., introduce Dogs-in-the-Wild, a comprehensive dog species dataset for fined-grained
classification. Ming Sun et al., also propose a new attention-based convolutional neural network that manages
different object parts among different input images [17]. In 2017 Yi Yu et al., work on very interesting idea,
they proposed a venue dataset. Venue dataset is a new type of dataset in case of FGVC, idea behind building
this dataset is to discover venue from any social image. Dataset include photos of venue, its description and
categories. Using this dataset they also proposed a venue discovery framework [18].
c. Cowbree vs. other datasets
Table 2 shows the detail of four datasets for FGVC, but all these four datasets are different from
Cowbree. Dataset by T. Sutojo et al., is somehow similar to Cowbree but it has a very less number of training
examples i.e. only 120 images belonging to 5 breeds of cow. While four targeted breeds in that particular
dataset are very rare and have low domestic use. The other three datasets in table are different in nature from
Cowbree, all three datasets target dog breeds.
Table 2. Detail of related datasets
No Author/Year Type Size Used Approach
1 T. Sutojo et al 2017 [19] Cow Breeds 120 images
5 categories
Content-based image retrieval model
2 Omkar M Parkhi et al 2012 [20] Cats and Dogs 7379 images
37 categories
Deformable part model
3 Ming Sun et al 2018 [17] Dog Species 299,458 images
362 categories
Attention-based convolutional neural network
(CNN)
4 Aditya Khosla et al 2011 [3] Dog Species 22,000 images
120 species
Bayesian incremental algorithm for learning
generative models
2. RESEARCH METHOD
Figure 2 explains the hierarchy of overall research process.
Figure 2. Overall workflow
2.1. Image acquisition
Image acquisition is the crucial and toughest part. More than twelve thousand images were collected
from different sources. Most of the images are collected from cattle markets of Punjab. But for few rare
breeds like jersey and Frisian that are popular breeds of Europe images are gathered from different web
sources. This collection of images for specific breeds of the cow was a very tough task, more than six persons
contributed to image acquisition part, and it took many months to gather more than 12,000 images.
1.
Image
Acquisition
Pre-Processing
(Image Selection + Manual Settings Cropping etc)
Feature Extraction
(Color & Texture features extraction by using
Gabor, AutoColorCorrelogram, FCTH filters)
Results
(Performance Matrices are Accuracy, Precision,
and Recall& F1 measure)
Classification
(By using SMO, J48 & Multiclass classifier)
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2.2. Image selection and pre-processings
As, this study collected enough number of images to build a dataset of different breeds of cow,
however, not all of the collected images are useful. For final selection of images from overall collected
images we have set following criteria:
a. Images should be collected from the natural environment, with background variations.
b. Those images should only be selected that contains a complete view of single animal, so that breed of
animals can be predicted accurately.
c. Images that are of high pixel quality should be selected for the final study.
On applying defined criteria more than thirty-three hundred images of eight different cow breeds are
selected for final study. In Figure 3 some examples of images that didn’t meet our criteria are shown, i.e. in
some pictures complete view of a single animal is not available or in some cases any other object disturbs the frame.
After image selection, next task is to separate the images according to the breed of animal that image contains.
This is the major and very difficult task of the whole study, this task needs very deep expert knowledge, by taking
the services of officials from the livestock and dairy development department of Pakistan we placed all the selected
images under their respective label i.e. breed names. Table 3 shows the exact numbers of images each label contain.
Figure 3. Images those are not fit for final study
Table 3. Showing the number of images each label contains
Name of breed Number of images
Cholistani 488
Sahiwal 630
Red Sindhi 563
Dhanni 577
Bhagnari 300
Friesian 432
Jersey 413
Cross-Breed 597
2.3. Feature extraction
After labeling the images Simple ARFF file (attribute-relation file format) of data is built by using
two attributes file name and class. The process of feature extraction is carried out through WEKA image
filter package. We have used threediffrent filters i.e. Auto Color Correlogram, Gabor and FCTH filter to
extract the features from data [21, 22]. For feature selection main target of the study is texture and color
features, as intra-class differences among the breeds are mainly body shape and color of the animal,
each selected filter have some specific chracteristics. Table 4 shows the basic description of each filter
and number of features extracted by each filter when it is applied on Cowbree dataset.
Table 4. Showing the basic description of filters
No Filter Detail #of features extracted from data
1
Auto Color
Correlogram
Filter
In our dataset beside texture difference among the different breeds of cow
the other main difference is their skin color for this particular reason we
have selected this filter to apply on our dataset. Auto Color Correlogram is
an Effective filter that robust to changes in viewing position and camera
zoom, it also calculates the degree of association of a picture with each
color [23].
1024
2 Gabor Filter
A linear filter used to extract texture features from image by using Gabor
wavelet transform, as in our data one of the main difference among
different breeds is the body shape texture of the animal [24].
59
3 FCTH filter
FCTH encode both color and texture information in one histogram, the size
of this filter is limited i.e. 72 bytes per image, so this filter is suitable for
use in large image datasets. Filter is very useful in our case as we have
large dataset and we also need both color and texture features of our
data [25].
191
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0
0,2
0,4
0,6
0,8
1
Sahiwal
Red
Sindhi
Jersey
Fresian
Desi
Cross
Breed
Cholistani
Bhagnaree
Precision
Recall
F-measure
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Sahiwal
Red
Sindhi
Jersey
Fresian
Desi
Cross
Breed
Cholistani
Bhagnaree
Precision
Recall
F-measure
3. RESULTS AND DISCUSSION
After feature extraction next task is to apply the classifiers to get the results of three matrices i.e.
accuracy, recall, and f-measure. In this work we have used three very well known state of the art classifiers
for classification experiments i.e. Sequential minimal optimization (SMO), Multiclass classifier and J48. Table 5
shows the results generated by classifiers when applied to all features extracted by three image filters. From Table
4 it is clear that, SMO performs better than other classifiers and show the good accuracy value of 68.81%. Now in
next step in Figures 4(a-c) we draw a graph to represent the comparison of different classes (cow breeds) with
respect to precision-recall and f-measure calculated by all three classifiers. This comparison helps us to find out
the class that affects overall results in positive and negative way.
Table 5. Result generated by classifiers
Name of classifier Precision Recall F Measure Accuracy
SMO 0.684 0.688 0.685 68.81%
J48 0.570 0.571 0.575 57.45%
Multiclass 0.556 0.558 0.556 55.81%
(a) (b)
(c)
Figure 4. (a) SMO, (b) J48, (c) multiclass classifier
Statistics in graphs show that class “crossbreed” affect negatively the overall results in all three
cases. Crossbreed class contains 597 examples but animal of this class differ from each other by means of
texture and color. Usually in all other classes the pattern of animal in images are much similar for example in
Sahiwal all the animals are of almost same color and their body structure is also quite similar but in
crossbreed class both color and structure of animal is different from one another. Similarly jersey class shows
the highest statistics in case of SMO and Multiclass classifier and performs positive in overall results. Class
Red Sindhi perform better in case of J48. As Jersey class contains images of very similar types of animals.
There is very little variation in color in all images of animals and body structure of animals in this class
is quite similar. Same is case with Red Sindhi, images of animals of Red Sindhi class also shows much
similarity with respect to color and body structure with each other.
0
0,2
0,4
0,6
0,8
Sahiwal
Red
Sindhi
Jersey
Fresian
Desi
Cross
Breed
Cholistani
Bhagnaree
Precision
Recall
F-measure
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4. CONCLUSION
We have collected a novel domain-specific image dataset of different cow breeds that consists of
4000 images belonging to eight different categories. We also conducted an experiment by using WEKA
image filter package for extracting the image features to calculate the baseline values of different matrices by
using three different classifiers. As we know Fine-grained visual categorization (FGVC) refers to categorize
of the objects belonging to one class but having the intra-class differences into subclasses. Collecting a fined
grained dataset is a very challenging task. It requires a deep knowledge of domain.
Deep neural network has been proven to be very successful in many applications in computer vision.
Therefore, we believe that one of the priorities in FGVC should be to implement techniques of deep learning
such as CNN etc. If experimental work on this dataset is implemented via deep learning the results may be
somehow different and the accuracy value can be increased. In the future by using this dataset, mobile app or
any other model can be trained that judges the breed of cattle by taking an image as input. In countries like
Pakistan, India, Bangladesh where cattle farming is a top business and the part of the life of a common
villager. This type of model or application helps them to select breeds more effectively. Besides this breed
detection, further this dataset can be used to train a model that can estimate the age of animal or maybe the weight of
animals by using different dimensions like height of animal, shape of animal and length of horns to estimate the age of
animal.
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BIOGRAPHIES OF AUTHORS
Umar Akbar Khan obtained his bachelor of science (Hons) in computer science in 2017 from
The University of Sargodha, Mianwali, Pakistan. He obtained MS degree in computer sciences
in 2019 from University of Lahore, Sargodha, Pakistan. His research intrest includes
classification, Fined-grained visual categorization.
Saira Moin U. Din obtained her MSC (IT) in 2011 from University of Sargodha (UOS),
Sargodha, Pakistan. She obtained MS degree in computer science degree in 2015 at University of
South Asia (USA), Lahore, Pakistan. She is prursing PhD computer science at University of
Sargodha, Pakistan. Her research interest includes classification, text proceasing, and social
network analysis.
Saima Anwar Lashari obtained her bachelor of science (Hons) in computer science in 2004 at
University Of Engineering & Technology (UET), Lahore, Pakistan. She holds MSc degree in
information technology in 2012 at UniversitiTun Hussein Onn Malaysia (UTHM), Malaysia. She
completed her PhD in information technology at UniversitiTun Hussein Onn Malaysia (UTHM),
Malaysia. Her research interest includes data mining, classification and soft set.
Murtaja Ali Saare is a Ph.D candidate at School of Computing, Sintok, Universiti Utara
Malaysia, Kedah, Malaysia. He holds a master’s degree in computer science. His research
interest includes aging and cognition, e-health and Human-Centered Computing. He has
published his research work inreputablescopus indexed journals.
Dr. Muhammad Ilyas has done PhD in Computer Science from Institute for Application
Oriented Knowledge Processing, Johannes Kepler University of Linz, Austria. Before That, he
completed MS in Software Project Management from National University of Computer and
Emerging Sciences, Lahore, Pakistan. His Research interest includes software engineering, information
retrieval.