The document proposes an image processing methodology to automatically grade disease on pomegranate leaves and identify bacterial blight disease. It involves image acquisition, pre-processing, color segmentation using k-means clustering to extract diseased spots, and calculating disease and total leaf areas to determine the percent infection and corresponding disease grade. The system can identify bacterial blight by checking leaves for yellow margins around diseased areas and fruits for cracks passing through black spots. The results of automatic grading are more accurate and less time-consuming than manual grading.
Pomegranate anthracnose is caused by the fungus Colletotrichum gleosporioides. It causes spotting and rotting of pomegranate fruits, reducing both price and quality. The fungus is found worldwide in tropical regions and spreads via infected leaves and wind-borne spores. High temperatures and humidity provide favorable conditions for growth of the pathogen. Chemical management includes fungicide sprays while biological control uses competitive fungi.
The document provides information on various diseases that affect horticultural crops. It discusses 16 different diseases affecting mangoes, including anthracnose caused by Colletotrichum gloeosporioides which causes spots on leaves, stems, flowers and fruits. It also discusses powdery mildew caused by Oidium mangiferae, mango malformation caused by Fusarium moliliforme, stem end rot caused by Diplodia natalensis and red rust caused by Cephaleuros virescens. It provides details on symptoms, pathogens, disease cycles and management practices for diseases of mangoes.
This document summarizes three cereal crops - barley, millet, and maize - and their main diseases. For barley, it describes powdery mildew, covered smut, and loose smut, including their pathogens and symptoms. For millet, it discusses green ear/downy mildew, grain smut, and ergot. And for maize, it covers smut, common rust, and anthracnose leaf blight, providing details on each disease's pathogen and symptoms. Control methods mentioned include using resistant varieties, fungicide seed treatment, spraying fungicides, field sanitation, and crop rotation.
This document provides an overview of plant disease identification, biology, and management in vegetable seed crops. It discusses various types of pathogenic organisms that cause diseases, including fungi, bacteria, viruses, and nematodes. The effects of seedborne diseases on yield, seed quality, and transmission to new crops are described. Methods for diagnosing diseases and managing them through cultural practices, chemical and biological seed treatments, and disease resistance are summarized.
This document discusses various sex forms in vegetables, including trimonoecy, monoecy, androecy, andromonoecy, gynoecy, gynomonoecy, hermaphrodite, dioecy, androdioecy, and gynodioecy. It provides examples of common sex forms in different cucurbits like cucumber, muskmelon, and ridge gourd. Environmental factors like temperature, photoperiod, and nitrogen levels can influence sex expression in cucurbits. Growth regulators including ethrel, NAA, IAA, IBA, and GA can be used to induce different sex forms for purposes like hybrid seed
This document discusses several major and minor insect pests that affect cucurbit crops. The four major pests covered are the red pumpkin beetle, cucurbits stink bug, pumpkin fruit fly, and spotted beetle. For each, details are provided on identification, life cycle, damage caused, and management strategies. The red pumpkin beetle feeds on plant parts both above and below ground, with all life stages causing damage. Management involves deep plowing, flooding, early sowing, and applying insecticides to soil. The cucurbits stink bug feeds on foliage and stems, with nymphs and adults both damaging plants. Management focuses on sanitation and using parasitoids and insecticide sprays. The
Onion smudge is caused by the fungus Colletotrichum circinans. It is a common disease of onions worldwide that occurs in temperate regions. The disease causes small dark spots on onion bulbs that can coalesce and reduce market value. Under moist conditions, spores are produced on the lesions that can spread the fungus. The fungus survives in soil and infected plant debris. Warm, moist conditions favor disease development. Management strategies include crop rotation, drainage, using disease-free seed and plant material, and fungicide applications before harvest.
Pomegranate anthracnose is caused by the fungus Colletotrichum gleosporioides. It causes spotting and rotting of pomegranate fruits, reducing both price and quality. The fungus is found worldwide in tropical regions and spreads via infected leaves and wind-borne spores. High temperatures and humidity provide favorable conditions for growth of the pathogen. Chemical management includes fungicide sprays while biological control uses competitive fungi.
The document provides information on various diseases that affect horticultural crops. It discusses 16 different diseases affecting mangoes, including anthracnose caused by Colletotrichum gloeosporioides which causes spots on leaves, stems, flowers and fruits. It also discusses powdery mildew caused by Oidium mangiferae, mango malformation caused by Fusarium moliliforme, stem end rot caused by Diplodia natalensis and red rust caused by Cephaleuros virescens. It provides details on symptoms, pathogens, disease cycles and management practices for diseases of mangoes.
This document summarizes three cereal crops - barley, millet, and maize - and their main diseases. For barley, it describes powdery mildew, covered smut, and loose smut, including their pathogens and symptoms. For millet, it discusses green ear/downy mildew, grain smut, and ergot. And for maize, it covers smut, common rust, and anthracnose leaf blight, providing details on each disease's pathogen and symptoms. Control methods mentioned include using resistant varieties, fungicide seed treatment, spraying fungicides, field sanitation, and crop rotation.
This document provides an overview of plant disease identification, biology, and management in vegetable seed crops. It discusses various types of pathogenic organisms that cause diseases, including fungi, bacteria, viruses, and nematodes. The effects of seedborne diseases on yield, seed quality, and transmission to new crops are described. Methods for diagnosing diseases and managing them through cultural practices, chemical and biological seed treatments, and disease resistance are summarized.
This document discusses various sex forms in vegetables, including trimonoecy, monoecy, androecy, andromonoecy, gynoecy, gynomonoecy, hermaphrodite, dioecy, androdioecy, and gynodioecy. It provides examples of common sex forms in different cucurbits like cucumber, muskmelon, and ridge gourd. Environmental factors like temperature, photoperiod, and nitrogen levels can influence sex expression in cucurbits. Growth regulators including ethrel, NAA, IAA, IBA, and GA can be used to induce different sex forms for purposes like hybrid seed
This document discusses several major and minor insect pests that affect cucurbit crops. The four major pests covered are the red pumpkin beetle, cucurbits stink bug, pumpkin fruit fly, and spotted beetle. For each, details are provided on identification, life cycle, damage caused, and management strategies. The red pumpkin beetle feeds on plant parts both above and below ground, with all life stages causing damage. Management involves deep plowing, flooding, early sowing, and applying insecticides to soil. The cucurbits stink bug feeds on foliage and stems, with nymphs and adults both damaging plants. Management focuses on sanitation and using parasitoids and insecticide sprays. The
Onion smudge is caused by the fungus Colletotrichum circinans. It is a common disease of onions worldwide that occurs in temperate regions. The disease causes small dark spots on onion bulbs that can coalesce and reduce market value. Under moist conditions, spores are produced on the lesions that can spread the fungus. The fungus survives in soil and infected plant debris. Warm, moist conditions favor disease development. Management strategies include crop rotation, drainage, using disease-free seed and plant material, and fungicide applications before harvest.
The document summarizes several diseases that affect marigold plants and their control methods. It describes diseases such as damping off caused by Rhizoctonia solani, leaf spots and blight caused by Alternaria, Cercospora and Septoria species, inflorescence blight caused by Alternaria zinnae, flower bud rot caused by Alternaria dianthi, and powdery mildew caused by Oidium sp. and Leveillula taurica. It provides details on symptoms, causal organisms, and recommendations for control which include soil drenching, fungicide spraying, and dusting with sulfur powder.
Citrus Greening Disease, also known as Huanglongbing (HLB), was first found in Florida in August 2005. It is a disease spread by the Asian citrus psyllid that infects citrus trees and eventually kills them. HLB has had severe economic impacts on Florida's citrus industry by reducing yields, increasing production costs, and requiring the removal of infected trees. Management of HLB is challenging as there are few effective treatment options and the disease and its vector have continued to spread rapidly throughout Florida's citrus-growing regions since being detected.
dIseases of cucurbits vegetables by MD. RAMJANmohammad ramjan
This document discusses several diseases that affect cucurbit crops including anthracnose, fruit rots caused by fungi, leaf spots, fusarium wilt, downy mildew, powdery mildew, alternaria blight, rhizoctonia root rot, mosaic diseases, seed rot and damping off, bacterial leaf spot, scab, cucumber mosaic virus, gummy stem blight, watermelon mosaic virus, and cucumber green mottle mosaic virus. It provides details on symptoms for each disease and recommends control measures such as using disease-free seed and crop rotation, applying fungicides and insecticides, and removing infected plant debris.
The document discusses several important diseases that affect turmeric plants, including rhizome root rot caused by Pythium fungi, dry rot caused by Rhizoctonia batalicola, and four foliar diseases: leaf blotch caused by Taphrina maculans, Colletotrichum leaf spot caused by Colletotrichum capsici, cercospora leaf spot, and leaf blight caused by Rhizoctonia solani. These diseases can affect turmeric plants at all stages and reduce rhizome yields considerably.
The document discusses several diseases that affect mango plants: anthracnose caused by Colletotrichum gloeosporioides which produces leaf spots and fruit rot; powdery mildew caused by Oidium mangiferae which affects leaves, flowers, and young fruits; mango malformation caused by Fusarium moniliforme var. subglutinans which results in stunted growth and malformed flowers and fruits; stem end rot caused by Botrydiplodia theobromae which causes rotting of the fruit; red rust caused by Cephaleurus mycoides which produces rust-colored spots on leaves; grey blight caused by Pestalotia mangiferae which causes brown leaf
Neem (नीम) is a Hindi noun derived from Sanskrit Nimba (निंब).
Neem is a fast-growing tree that can reach a height of 15–20 metres (49–66 ft), and rarely 35–40 metres (115–131 ft).
It is evergreen, but in severe drought it may shed most of its leaves or nearly all leaves
The branches are wide and spreading.
The neem tree is very similar in appearance to its relative, the Chinaberry
Verticillium wilt of brinjal is caused by the soilborne fungi Verticillium dahliae and Verticillium albo-atrum. The disease was first reported in North America in the 1940s. It causes economic losses worldwide in temperate regions by infecting many hosts including brinjal, tomato, cotton and potato. Symptoms include leaf yellowing, wilting, and plant death. The fungi survive in soil and plant debris as microsclerotia or melanized mycelia between crops. Management strategies include crop rotation, removing crop residues, and soil fumigation or fungicide dips to control spread.
Chickpea (Cicer arietinum L.) is the most important pulse crop in India, occupying about 38% of pulse crop area and contributing around 50% of total pulse production. It is a rich source of protein and essential nutrients. India is the world's largest producer and consumer of chickpeas. Chickpea cultivation requires moderate rainfall, well-drained sandy loam to clay loam soils, and optimum sowing times of mid-October in northern India and early October in peninsular India. Proper seedbed preparation and crop rotation are important for good yields of this winter season pulse crop.
This document summarizes 14 common fungal diseases that affect okra plants. It describes the symptoms and control methods for each disease. The diseases include leaf spots caused by fungi like Cercospora, powdery mildew caused by Erysiphe, and blossom blight caused by Choanephora. Control methods involve cultural practices like removing infected plant material, improving air circulation, and chemical controls like applying appropriate fungicides. Proper disease management is important to prevent yield loss and maintain okra plant health.
1. The document discusses several diseases that affect betelvine crops including foot rot caused by Phytophthora parasitica var. piperina, sclerotium foot rot and wilt caused by Sclerotium rolfsii, powdery mildew caused by Oidium piperis, bacterial leaf spot caused by Xanthomonas campestris pv. betlicola, and anthracnose caused by Colletotrichum piperis.
2. It describes the symptoms, pathogens, favorable conditions, modes of spread and survival, and management practices for each disease.
3. The management strategies include removing and destroying infected plant material, applying fungicides and bactericides,
The document discusses common rice diseases found in Bangladesh. It identifies 31 total rice diseases, with 10 considered major. These major diseases include bacterial blight, bacterial leaf streak, sheath blight, blast, brown spot, narrow brown leaf spot, false smut, and rice tungro viral disease. For each disease, the document discusses the causal pathogen, symptoms, and management recommendations. Key management strategies include using resistant varieties, crop rotation, proper fertilization and irrigation, and fungicide application.
All virus diseases and its vectors in field cropsvasanthkumar650
This document provides information on viral diseases and their vectors in various field crops. It lists the crop, viral disease, causative virus, symptoms, and vector for numerous cereal, pulse, oilseed, fibre, sugar, narcotic, and mulberry crops. The diseases covered include rice tungro, ragged stunt of rice, rice yellow dwarf, rice grassy stunt, barley yellow dwarf, maize stripe, maize streak, maize dwarf mosaic, mottle streak of ragi, sterility mosaic of pigeonpea, yellow mosaic of mungbean and blackgram, leaf crinkle and leaf curl/necrosis of blackgram, cowpea mosaic, cowpea aphid borne mosaic
- Guava anthracnose is caused by the fungal pathogen Gloeosporium psidii. It affects guava plants and fruits.
- Symptoms include die back of branches, leaf spots, and sunken lesions on fruits. The disease is favored by moist conditions and temperatures between 10-35°C.
- The pathogen can survive on plant debris and spreads via airborne spores. Management involves resistant varieties, pruning, fungicide sprays, and post-harvest fruit dips.
This document summarizes several common diseases that affect aloe vera plants: base rot caused by Pectobacterium chrysanthemi bacteria, leaf spot caused by the fungus Alternaria alternata, aloe rust caused by Phakopsora pachyrhizi fungus, sooty mould caused by various fungi growing on honeydew from insects, and basal stem rot caused by Fusarium fungi. It provides details on symptoms, pathogens, and conditions that favor disease development for some of these diseases, as well as integrated disease management practices like removing infected plants, crop rotation, and fungicide spraying.
This document discusses the early blight disease of tomatoes caused by the fungus Alternaria solani. It describes the pathogen, including its scientific classification and physical characteristics. The document outlines the disease symptoms which include brown-black leaf spots and stem lesions. It also covers the disease epidemiology, including favorable warm, wet conditions for spread. Management strategies discussed are cultural controls like crop rotation and debris removal, as well as chemical controls using fungicides applied every 15-20 days.
Combination fungicides in india and their usesSubhomay Sinha
This document provides information on 12 combination/pre-mix fungicide formulations that are registered in India and their uses against plant pathogens. It describes the active ingredients and modes of action of each formulation and lists the plant diseases they are effective against. The formulations include combinations of azoxytrobin/tebuconazole, azoxystrobin/difenoconazole, boscalid/pyraclostrobin, ametoctradin/dimethomorph, captan/hexaconazole, carbendazim/mancozeb, carbendazim/flusilazole, carboxin/thiram, cymoxanil/mancozeb,
The document discusses the flat limb disease of sapota, caused by the fungus Botryodiplodia theobromae. The disease causes twisting and flattening of sapota branches, resulting in fewer and smaller fruits. It is most prevalent in India in states like Maharashtra, Gujarat, Tamil Nadu, Karnataka, West Bengal and Andhra Pradesh, with integrated management including pruning of infected branches, fungicide application and destruction of plant debris.
The document provides information on diseases that affect cotton plants (Gossypium spp.), including bacterial blight, fusarium wilt, verticillium wilt, and root rot. It describes the symptoms, causal pathogens, disease cycles, and favorable conditions for each disease. Management strategies are also outlined, such as using resistant varieties, seed treatment, crop rotation, removing debris, and adjusting sowing times. The overall objective is to familiarize the reader with common cotton diseases and their control.
You will have to know about major diseases of plam in this presentation. all the factors are covered in it. i tried my best to give you the complete information about plan diseases.
People first counted using their fingers and stones. Later, mathematician John Napier invented calculating rods called Napier bones to help with multiplication. A young French mathematician named Blaise Pascal invented an early adding machine while working in his father's office adding tax columns, making his work faster.
The document summarizes several diseases that affect marigold plants and their control methods. It describes diseases such as damping off caused by Rhizoctonia solani, leaf spots and blight caused by Alternaria, Cercospora and Septoria species, inflorescence blight caused by Alternaria zinnae, flower bud rot caused by Alternaria dianthi, and powdery mildew caused by Oidium sp. and Leveillula taurica. It provides details on symptoms, causal organisms, and recommendations for control which include soil drenching, fungicide spraying, and dusting with sulfur powder.
Citrus Greening Disease, also known as Huanglongbing (HLB), was first found in Florida in August 2005. It is a disease spread by the Asian citrus psyllid that infects citrus trees and eventually kills them. HLB has had severe economic impacts on Florida's citrus industry by reducing yields, increasing production costs, and requiring the removal of infected trees. Management of HLB is challenging as there are few effective treatment options and the disease and its vector have continued to spread rapidly throughout Florida's citrus-growing regions since being detected.
dIseases of cucurbits vegetables by MD. RAMJANmohammad ramjan
This document discusses several diseases that affect cucurbit crops including anthracnose, fruit rots caused by fungi, leaf spots, fusarium wilt, downy mildew, powdery mildew, alternaria blight, rhizoctonia root rot, mosaic diseases, seed rot and damping off, bacterial leaf spot, scab, cucumber mosaic virus, gummy stem blight, watermelon mosaic virus, and cucumber green mottle mosaic virus. It provides details on symptoms for each disease and recommends control measures such as using disease-free seed and crop rotation, applying fungicides and insecticides, and removing infected plant debris.
The document discusses several important diseases that affect turmeric plants, including rhizome root rot caused by Pythium fungi, dry rot caused by Rhizoctonia batalicola, and four foliar diseases: leaf blotch caused by Taphrina maculans, Colletotrichum leaf spot caused by Colletotrichum capsici, cercospora leaf spot, and leaf blight caused by Rhizoctonia solani. These diseases can affect turmeric plants at all stages and reduce rhizome yields considerably.
The document discusses several diseases that affect mango plants: anthracnose caused by Colletotrichum gloeosporioides which produces leaf spots and fruit rot; powdery mildew caused by Oidium mangiferae which affects leaves, flowers, and young fruits; mango malformation caused by Fusarium moniliforme var. subglutinans which results in stunted growth and malformed flowers and fruits; stem end rot caused by Botrydiplodia theobromae which causes rotting of the fruit; red rust caused by Cephaleurus mycoides which produces rust-colored spots on leaves; grey blight caused by Pestalotia mangiferae which causes brown leaf
Neem (नीम) is a Hindi noun derived from Sanskrit Nimba (निंब).
Neem is a fast-growing tree that can reach a height of 15–20 metres (49–66 ft), and rarely 35–40 metres (115–131 ft).
It is evergreen, but in severe drought it may shed most of its leaves or nearly all leaves
The branches are wide and spreading.
The neem tree is very similar in appearance to its relative, the Chinaberry
Verticillium wilt of brinjal is caused by the soilborne fungi Verticillium dahliae and Verticillium albo-atrum. The disease was first reported in North America in the 1940s. It causes economic losses worldwide in temperate regions by infecting many hosts including brinjal, tomato, cotton and potato. Symptoms include leaf yellowing, wilting, and plant death. The fungi survive in soil and plant debris as microsclerotia or melanized mycelia between crops. Management strategies include crop rotation, removing crop residues, and soil fumigation or fungicide dips to control spread.
Chickpea (Cicer arietinum L.) is the most important pulse crop in India, occupying about 38% of pulse crop area and contributing around 50% of total pulse production. It is a rich source of protein and essential nutrients. India is the world's largest producer and consumer of chickpeas. Chickpea cultivation requires moderate rainfall, well-drained sandy loam to clay loam soils, and optimum sowing times of mid-October in northern India and early October in peninsular India. Proper seedbed preparation and crop rotation are important for good yields of this winter season pulse crop.
This document summarizes 14 common fungal diseases that affect okra plants. It describes the symptoms and control methods for each disease. The diseases include leaf spots caused by fungi like Cercospora, powdery mildew caused by Erysiphe, and blossom blight caused by Choanephora. Control methods involve cultural practices like removing infected plant material, improving air circulation, and chemical controls like applying appropriate fungicides. Proper disease management is important to prevent yield loss and maintain okra plant health.
1. The document discusses several diseases that affect betelvine crops including foot rot caused by Phytophthora parasitica var. piperina, sclerotium foot rot and wilt caused by Sclerotium rolfsii, powdery mildew caused by Oidium piperis, bacterial leaf spot caused by Xanthomonas campestris pv. betlicola, and anthracnose caused by Colletotrichum piperis.
2. It describes the symptoms, pathogens, favorable conditions, modes of spread and survival, and management practices for each disease.
3. The management strategies include removing and destroying infected plant material, applying fungicides and bactericides,
The document discusses common rice diseases found in Bangladesh. It identifies 31 total rice diseases, with 10 considered major. These major diseases include bacterial blight, bacterial leaf streak, sheath blight, blast, brown spot, narrow brown leaf spot, false smut, and rice tungro viral disease. For each disease, the document discusses the causal pathogen, symptoms, and management recommendations. Key management strategies include using resistant varieties, crop rotation, proper fertilization and irrigation, and fungicide application.
All virus diseases and its vectors in field cropsvasanthkumar650
This document provides information on viral diseases and their vectors in various field crops. It lists the crop, viral disease, causative virus, symptoms, and vector for numerous cereal, pulse, oilseed, fibre, sugar, narcotic, and mulberry crops. The diseases covered include rice tungro, ragged stunt of rice, rice yellow dwarf, rice grassy stunt, barley yellow dwarf, maize stripe, maize streak, maize dwarf mosaic, mottle streak of ragi, sterility mosaic of pigeonpea, yellow mosaic of mungbean and blackgram, leaf crinkle and leaf curl/necrosis of blackgram, cowpea mosaic, cowpea aphid borne mosaic
- Guava anthracnose is caused by the fungal pathogen Gloeosporium psidii. It affects guava plants and fruits.
- Symptoms include die back of branches, leaf spots, and sunken lesions on fruits. The disease is favored by moist conditions and temperatures between 10-35°C.
- The pathogen can survive on plant debris and spreads via airborne spores. Management involves resistant varieties, pruning, fungicide sprays, and post-harvest fruit dips.
This document summarizes several common diseases that affect aloe vera plants: base rot caused by Pectobacterium chrysanthemi bacteria, leaf spot caused by the fungus Alternaria alternata, aloe rust caused by Phakopsora pachyrhizi fungus, sooty mould caused by various fungi growing on honeydew from insects, and basal stem rot caused by Fusarium fungi. It provides details on symptoms, pathogens, and conditions that favor disease development for some of these diseases, as well as integrated disease management practices like removing infected plants, crop rotation, and fungicide spraying.
This document discusses the early blight disease of tomatoes caused by the fungus Alternaria solani. It describes the pathogen, including its scientific classification and physical characteristics. The document outlines the disease symptoms which include brown-black leaf spots and stem lesions. It also covers the disease epidemiology, including favorable warm, wet conditions for spread. Management strategies discussed are cultural controls like crop rotation and debris removal, as well as chemical controls using fungicides applied every 15-20 days.
Combination fungicides in india and their usesSubhomay Sinha
This document provides information on 12 combination/pre-mix fungicide formulations that are registered in India and their uses against plant pathogens. It describes the active ingredients and modes of action of each formulation and lists the plant diseases they are effective against. The formulations include combinations of azoxytrobin/tebuconazole, azoxystrobin/difenoconazole, boscalid/pyraclostrobin, ametoctradin/dimethomorph, captan/hexaconazole, carbendazim/mancozeb, carbendazim/flusilazole, carboxin/thiram, cymoxanil/mancozeb,
The document discusses the flat limb disease of sapota, caused by the fungus Botryodiplodia theobromae. The disease causes twisting and flattening of sapota branches, resulting in fewer and smaller fruits. It is most prevalent in India in states like Maharashtra, Gujarat, Tamil Nadu, Karnataka, West Bengal and Andhra Pradesh, with integrated management including pruning of infected branches, fungicide application and destruction of plant debris.
The document provides information on diseases that affect cotton plants (Gossypium spp.), including bacterial blight, fusarium wilt, verticillium wilt, and root rot. It describes the symptoms, causal pathogens, disease cycles, and favorable conditions for each disease. Management strategies are also outlined, such as using resistant varieties, seed treatment, crop rotation, removing debris, and adjusting sowing times. The overall objective is to familiarize the reader with common cotton diseases and their control.
You will have to know about major diseases of plam in this presentation. all the factors are covered in it. i tried my best to give you the complete information about plan diseases.
People first counted using their fingers and stones. Later, mathematician John Napier invented calculating rods called Napier bones to help with multiplication. A young French mathematician named Blaise Pascal invented an early adding machine while working in his father's office adding tax columns, making his work faster.
This document contains biographical information about multiple individuals who passed away between 1980 and 2013. It includes their dates of birth and death, ages, affiliations, occupations, and in some cases brief details about their interests and families. The individuals commemorated ranged in age from 18 to 50 years old at the time of their passing. Many were noted to be graduates of or affiliated with local schools and churches in the Johnstown, Pennsylvania area.
This short document promotes creating presentations using Haiku Deck on SlideShare. It encourages the reader to get started making their own Haiku Deck presentation by providing a button to click to begin the process. The document is advertising the creation of presentations on Haiku Deck and SlideShare.
Sarah Alvis is a junior who enjoys reading and some activities. She states that she likes reading, breathing, and some stuff, though something else may be better. Additionally, she notes that she is American.
This document introduces a Multi-Dimensional Index of Nutrition (NeSNI) to better communicate the complexity of nutrition issues to policymakers. The NeSNI ranks countries based on 6 nutrition indicators: stunting, anemia, low birth weight, childhood overweight, exclusive breastfeeding, and wasting. It shows how countries perform on the overall index and individual indicators. The document discusses how the NeSNI can help track progress towards global nutrition targets and clarify how different policy areas and sectors like agriculture can impact nutrition outcomes.
Ash Ketchum is the main character from the Pokémon cartoon series. He wears the same clothes in every season and is always accompanied by his Pokémon partner Pikachu. Ash travels around different regions challenging gym leaders and collecting badges by battling with his Pokémon. He maintains a consistently positive attitude even when facing challenges and encourages others to keep trying. Other characters see Ash as a skilled trainer and good friend.
Cleveland Brown is a family man from Stoolbend, Virginia who works and eats a lot. He has a mustache, wears a yellow shirt and blue pants, and says funny things. His son looks up to him, though he does not think very much himself.
The document introduces the Tizen SDK, which provides a comprehensive set of tools for developing Tizen applications. The SDK includes an IDE, emulators, debugging tools, and supports development of both web and native applications. It aims to be specialized for Tizen, support multiple devices and hosts, provide an all-in-one development environment, and offer rich features. The SDK is based on open technologies and its architecture is extensible to customize the Tizen platform.
Bubbles is one of the Power Puff Girls who lives in Townsville, USA. She has blonde pigtails, big blue eyes, pale skin, and wears a blue dress with white leggings and black shoes while fighting monsters and protecting the city with her sisters. Bubbles thinks often about her stuffed octopus and her sisters' safety, loves animals, and can emit supersonic waves from her voice.
IRJET- Disease Detection in the Leaves of Multiple PlantsIRJET Journal
1. The document proposes a deep learning-based method to detect diseases affecting the leaves of multiple plant varieties.
2. A convolutional neural network (CNN) model called ResNet-50 is trained on 1,222 images of diseased plant leaves representing 5 different diseases.
3. The CNN model achieves over 96% classification accuracy, outperforming conventional machine learning techniques for disease detection.
This document presents a proposed system for disease diagnosis of mango leaves using image processing techniques. The system uses a three-step process: 1) Image analysis to preprocess leaf images and extract affected regions, 2) Feature extraction of color and texture characteristics from affected regions, 3) Classification of leaf diseases based on extracted features using a trained machine learning model. The proposed system is intended to help agricultural specialists and farmers diagnose leaf diseases early and accurately by analyzing digital images of affected leaves. Some key advantages of the system include being low-cost, time-efficient, and able to diagnose multiple diseases. The system was tested on a dataset of 129 mango leaf images with promising 89.92% accuracy in identifying three common mango diseases.
Food is one of the basic needs of human being. Population is increasing day by day. So, it has become important to grow sufficient amount of crops to feed such a huge population. Agricultural intervention in the livelihood of rural India is about 58%. But with the time passing by, plants are being affected with many kinds of diseases, which cause great harm to the agricultural plant productions. It is very difficult to monitor the plant diseases. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing speed and time. Hence, image processing is used for the detection of plant diseases by just capturing the images of the leaves and comparing it with the data sets available. Latest and fostering technologies like Image processing is used to rectify such issues very effectively. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. This paper aims to support and help the farmers in an efficient way.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
RICE PLANT DISEASE DETECTION AND REMEDIES RECOMMENDATION USING MACHINE LEARNINGIRJET Journal
This document describes a machine learning approach to detect diseases in rice plants from images and recommend remedies. It discusses three common rice diseases - leaf blast, bacterial leaf blight, and hispa - and how a convolutional neural network was trained on thousands of images to classify diseases. The proposed method uses CNN layers to extract features from images and fully connected layers to classify diseases. It aims to help farmers early detect diseases from photos and provide effective treatment recommendations to improve crop yields.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
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Bacterial foraging optimization based adaptive neuro fuzzy inference system IJECEIAES
Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.
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Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
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Plant Disease Detection Technique Using Image Processing and machine LearningJitendra111809
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An Innovative Approach for Tomato Leaf Disease Identification and its Benefic...IRJET Journal
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This document describes a research project on crop disease detection using image processing and machine learning. The authors aim to develop a system that can recognize plant diseases from images of leaves by analyzing color, texture, and shape. The system would classify diseases using algorithms like convolutional neural networks (CNN), artificial neural networks (ANN), support vector machines (SVM), and fuzzy logic. This automatic disease detection could help farmers identify issues early and apply the proper treatments to prevent crop destruction and financial losses. The methodology captures leaf images and uses machine learning models trained on symptom features to diagnose common diseases like early rot and bacterial spots. The goal is to provide farmers with a fast and accurate disease identification tool.
Plant Leaf Disease Detection Using Machine LearningIRJET Journal
This document describes a research project that uses machine learning to detect plant leaf diseases. Specifically, it uses a Convolutional Neural Network (CNN) model trained on the PlantVillage dataset to classify images and identify 15 common diseases in tomato, potato, and pepper plants. The system is implemented as a web application that farmers can use to upload images of plant leaves. It provides disease names and treatment recommendations to help farmers efficiently diagnose issues and select appropriate pesticides. Validation tests found the system could accurately identify diseases 93.5% of the time. The goal is to help farmers, especially small-scale farmers, save crops and income by enabling easy, early detection of plant diseases.
IRJET- Agricultural Seed Disease Detection using Image Processing TechniqueIRJET Journal
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The document presents a high-density, high-efficiency isolated on-board vehicle battery charger utilizing silicon carbide power devices. The charger uses a two-stage architecture with a bridgeless boost ac-dc converter for the first stage and a phase-shifted full-bridge isolated dc-dc converter for the second stage. Experimental results show a peak efficiency of 95% and maximum output power of 6.1 kW, achieving a volumetric power density of 5.0 kW/L and gravimetric power density of 3.8 kW/kg. This represents over a 10x increase in power density compared to previous vehicle battery chargers.
ADVANCED BIKE SECURITY SYSTEM USING GSM AND GPSEG TECHNOLOGIES
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Real time parameter estimation for power quality control and intelligent prot...EG TECHNOLOGIES
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Film vocab for eal 3 students: Australia the movie
Pomegranate fuzzy
1. Grading & Identification of Disease in
Pomegranate Leaf and Fruit
Tejal Deshpande1
, Sharmila Sengupta2
, K.S.Raghuvanshi 3
1
Assistant professor, Electronics & Telecommunication, Xavier Institute of Engg
2
Assistant professor, Electronics & Telecommunication, Vivekanand Institute of Technology
1,2
University of Mumbai, India
3
Associate professor, Plant pathology, Mahatma Phule Krishi Vidyapeeth
3
Mahatma Phule agriculture university ,Rahuri, Ahmednagar India
Abstract - Present paper is an attempt to automatically grade
the disease on the Pomegranate plant leaves. This innovative
technique would be a boon to many and would have a lot of
advantages over the traditional method of grading. There has
been a sea change in the mindset and the effort put down by
the agricultural industry by adapting to the current trends &
technologies. One such example is the use of Information and
Communication Technology (ICT) in agriculture which
eventually contributes to Precision Agriculture. Presently,
plant pathologists follow a tedious technique that mainly relies
on naked eye prediction and a disease scoring scale to grade
the disease. Manual grading is not only time consuming but
also does not give precise results. Hence the current paper
proposes an image processing methodology to deal with one of
the main issues of plant pathology i.e disease grading. The
results are proved to be accurate and satisfactory in contrast
to manual grading and hopefully take a strong leap forward in
establishing itself in the market as one of the most efficient and
effective process.
The proposed system is also an efficient module that identifies
the Bacterial Blight disease on pomegranate plant. At first, the
captured images are processed for enhancement. Then image
segmentation is carried out to get target regions (disease spots)
on the leaves and fruits. Later, if the diseased spot on leaf is
bordered by yellow margin then it is said that leaf is infected
by bacterial blight otherwise not. Similarly when black spots
are targeted on fruits, they are checked for whether a crack is
passing through these spots. If cracks are passing through the
spots then the disease identified would be Bacterial blight.
Based on these two characteristics bacterial blight on
pomegranate can be appropriately identified.
Keywords— Percent Infection, Bacterial Blight, K-means
clustering, Morphology, colour image segmentation, Precision
agriculture.
I. INTRODUCTION
Sole area that serves the food needs of the entire human
race is the Agriculture sector. Research in agriculture is
aimed towards increase of productivity and food quality at
reduced expenditure and with increased profit [1]. In the
past few years new trends have emerged in the agricultural
sector. Due to the manifestation and developments in the
fields of sensor networks, robotics, GPS technology,
communication systems etc, precision agriculture started
emerging [2]. Precision agriculture concentrates on
providing the means for observing, assessing and
controlling agricultural practices. It also takes into account
the pre- and post-production aspects of agricultural
enterprises. The objectives of precision agriculture are
profit maximization, agricultural input rationalization and
environmental damage reduction, by adjusting the
agricultural practices to the site demands. The challenge of
the precision approach is to equip the farmer with adequate
and affordable information and control technology.
Plant disease is one of the crucial causes that reduces
quantity and degrades quality of the agricultural products.
Disease is impairment to the normal state of the plant that
modifies or interrupts its vital functions such as
photosynthesis, transpiration, pollination, fertilization,
germination etc. The emergence of plant diseases has
become more common now days, as factors such as
climate and environmental conditions are more unsettled
than ever [2]. Plant diseases are usually caused by fungi,
bacteria and viruses. Also there are other diseases which
are caused by adverse environmental conditions. There are
numerous characteristics and behaviours of such plant
diseases in which many of them are merely
distinguishable. The ability of disease diagnosis in earlier
stage is an important task. Hence an intelligent decision
support system for Prevention and Control of plant
diseases is needed. This system uses some high-tech and
practical technology to appropriately detect and diagnose
the plant diseases. Technological advancement is
gradually finding its applications in the field of agriculture
[3]. The information and communication technology (ICT)
application is going to be implemented as a solution in
improving the status of the agricultural sector [4]. The idea
of integrating ICT with agriculture sector motivates the
development of an automated system for pomegranate
disease classification and its grading.
Pomegranate (Punica granatum), so called “fruit of
paradise” is one of the major fruit crops of arid region. It is
Popular in Eastern as well as Western parts of the world.
The fruit is grown for its attractive, juicy, sweet-acidic and
fully luscious grains called ‘Arils’ [6]. The fruits are
mainly used for dessert purposes. In India it is cultivated
over the area of about 63,000 ha, and its production is
about 5 lakh tons/annum. Important varieties cultivated are
Ganesh, Dholka, Seedless(Bedana), Bhagwa, Araktha.
Figure1 shows three varieties of pomegranate fruit.
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
www.ijcsit.com 4638
2. a) Bhagwa (b) Ganesh (c) Araktha
Figure1- Varieties of pomegranate
Based on size and colour, pomegranate fruits are graded as
follows:
Super size- in which fruits are free from spots and
individual fruit weight is more than750grams.
King size –in which fruits are attractive and individual
fruit weight is 500-700 grams.
Queen size- in which fruits are attractive, red and
individual fruit weight is 400-500 grams.
Prince size- in which fruits are attractive, red and
individual fruit weight is 300-400 grams.
Unfortunately there are no organized marketing systems
for pomegranate. Usually farmers dispose their produce to
contractors who will later transport too far off markets.
There is scope for exporting Indian pomegranates to
Bangladesh, Bahrain, Canada, Germany, United Kingdom,
Japan, Kuwait, Sri Lanka, Omen, Pakistan, Qatar, Saudi
Arabia, Singapore, Switzerland, U.A.E. and U.S.A.
Diseases and insect pests are the major problems that
threaten pomegranate cultivation. These require careful
diagnosis and timely handling to protect the crops from
heavy losses [6]. In pomegranate plant, diseases can be
found in various parts such as fruit, stem and leaves. Major
diseases that affect pomegranate fruit are bacterial blight
(Xanthomonas axonopodis pv punicae), antracnose
(Colletotrichum gloeosporoides) and wilt complex
(ceratocystis fimbriata).
Image samples of these diseases are shown in Figure 2.
Wilt Complex
Anthracnose
Scab
Bacterial Blight on fruit
Bacterial Blight on leaf
Bacterial Blight on Stem
Figure2- Various diseases affecting pomegranate
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
www.ijcsit.com 4639
3. Bacterial blight is the most severe disease of the
pomegranate. The disease symptoms can be initially found
on stem part which gradually pervades to leaves and then to
fruits. On stem, the disease starts as brown to black spot
around the nodes. In advance stages of nodal infection
girdling and cracking of nodes lead to break down of
branches. On leaves, the disease starts with small, irregular,
water soaked spots that are 2 to 5 mm in size with necrotic
centre of pin head size. Spots are translucent against light.
Later, these spots turn light to dark brown and are
surrounded by prominent yellow margin. Numerous spots
may coalesce to form bigger patches. Severely infected
leaves may drop off. On fruits brown to black spots appear
on pericap with cracks passing through the spots. The
disease spreads as the bacterium survives on the tree as well
as in diseased fallen leaves. High temperature and high
relative humidity favours the disease. The disease spreads
to healthy plants through wind splashed rains and in new
area through infected cuttings.
II. MANUAL GRADING : PRESENT APPROACH
Plants are bound to have diseases. The infected plants are
diagnosed and treatment is suggested to cure the disease. To
treat the disease chemical pesticides are used. Pesticides are
substances or mixture of substances intended for
preventing, destroying, repelling or mitigating any pest.
Chemicals are continually becoming a more intricate part of
modern society. The rampant use of these chemicals, under
the adage, “if little is good, a lot more will be better” has
played havoc with human and also on agricultural products.
Use of these toxic chemicals can only be minimised when
the disease is identified accurately along with the stage in
which the disease is observed.
Presently, the plant pathologists rely on disease scoring
scale to grade the disease. This is shown in Table 1.
Table 1: Disease Scoring Scale for Leaves
From table 1, it is observed that the grade of the disease is
assigned based on the percent-infection i.e., if the infection
percent is about 5 then the grade is 2. The problem here is
that even if the percent-infection is 2 or 9 the grade remains
2 only.
Presently, percent-infection is calculated based on grid
paper analysis. This method is time consuming and burden
of repetitive tasks. Since human intervention is involved, it
is prone to errors also. Hence, now the time has come to
overcome these problems. This can be achieved by
inculcating machine vision into the agriculture to get the
accurate grade of the disease. With this motto the present
paper proposes an automatic and accurate disease grading
system for plant leaves which can be of great use for the
agronomists. For the experimentation purpose,
pomegranate leaves are considered.
III. METHODOLOGY
Proposed method will grade and identify Bacterial Blight
disease of pomegranate leaf and fruit.
The system architecture is presented in figure2.
The system is divided into the following steps: (1) Image
acquisition (2) Image Pre-processing (3) Colour image
segmentation (4) Calculating AT and AD (5) Disease
grading
A. System Architecture
Figure2: System architecture
B. Image Acquisition
First stage of any vision system is the image acquisition
stage. The digitization and storage of an image is referred as
the image acquisition. After the image has been obtained,
various methods of processing can be applied to the image
to perform the many different vision tasks required today.
However, if the image has not been acquired satisfactorily
then the intended tasks may not be achievable, even with
the aid of some form of image enhancement.
All the images are saved in the JPEG format. For the
purpose of image acquisition, author has visited and
captured images from several pomegranate farms in Rahuri,
Ahmednagar district, Maharashtra, India.
C. Image Pre-Processing
Pre-processing images commonly involves removing low-
frequency background noise, normalizing the intensity of
the individual particles images, removing reflections, and
masking portions of images[1]. Image pre-processing is the
technique of enhancing data images prior to computational
processing.
Image processing is a form of signal processing for which
the input is an image, such as a photograph or video frame;
the output of image processing may be either an image or a
set of characteristics or parameters related to the image [1].
Pre-processing uses the techniques such as image resize,
erosion, dilation, segmentation, cropping, etc.
Initially, captured images are resized to a fixed resolution
so as to utilize the storage capacity or to reduce the
computational burden in the later processing. Shadow may
or not be there image acquisition. Shadow would disturb the
segmentation and the feature extraction of disease spots. So
it must be removed or weakened before any further image
Percent Infection Disease Grade
0 – 1 1
1-10 2
10-20 3
20-40 4
40-100 5
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
www.ijcsit.com 4640
4. analysis by applying shadow removal algorithms which
makes use of morphology. In the present work, author has
considered erosion, dilation operations for getting better
results.
D. Image Segmentation
Image segmentation refers to the process of partitioning the
digital image into its constituent regions or objects so as to
change the representation of the image into something that
is more meaningful and easier to analyze. The level to
which the partitioning is carried depends on the problem
being solved i.e. segmentation should stop when the objects
of interest in an application have been isolated [5]. In the
current work, the very purpose of segmentation is to
identify regions in the image that are likely to qualify as
diseased regions. There are various techniques for image
segmentation. K-means clustering method has been used in
the present work to carry out segmentation. K-Means
Clustering is a method of cluster analysis which aims to
partition n observations into k mutually exclusive clusters in
which each observation belongs to the cluster with the
nearest mean.
When the segmentation is completed, one of the clusters
contains the diseased spots being extracted. This image is
saved and considered for calculating AD.
E. Calculating AT and AD
In image processing terminology area of a binary image is
the total number of on pixels in the image[1]. Hence, the
original resized image is converted to binary image such
that the pixels corresponding to the leaf image are on. From
this image total leaf area (AT) is calculated. Similarly, the
output image from color image segmentation, containing
the disease spots, is used to calculate total disease area
(AD)[1].
F. Disease Grading
Once AT and AD are known, the percent-infection (PI) is
calculated by applying the formula (1).
PI= (AD / AT ) *100 . . . (1)
Grade of the disease has to be determined from PI.
Percent Infection Disease Grade
0 0
0.1-5.999 1
6-15.999 2
16-25.999 3
26-35.999 4
36-45.999 5
46-55.999 6
56-65.999 7
66-75.999 8
76-100 9
Table2: Disease Scoring Scale for Leaves
Grading Process is shown in figure3.
G. Disease Detection
Image Pre-processing is carried out on the acquired image.
Shadow removal and image correction algorithm are used
in this stage. For removing shadow morphology
fundamentals such as erosion and dilation are used. In
image post processing the interested part is extracted by
using K-Means clustering and its features are analyzed. If
the leaf containing brown to black spot is bordered by
yellow margin, denotes that the leaf is infected by Bacterial
Blight. For fruits first the black spots are identified and if
there is a crack passing through that black spot it signifies
that the fruit is infected by Bacterial Blight. Cracks are
found using canny edge detector.
Once the disease is identified, grading is done so that
appropriate treatment advisory can be provided by seeking
the help from agricultural experts so that the disease can be
prevented from further spreading.
Disease detection process is shown in figure4.
IV. DESIGN
A. Flowchart for Grading
Figure3: Grading Process
Start
Image Acquisition
Image Resize
Image Pre-processing
(Image correction,
Shadow Removal)
Colour Image
Segmentation (KMeans)
Clustering)
Diseased spot extracted
C
C
Calculate Disease Area
(Ad)
Covert the original
image to B/W image
Calculate Total Area
(At)
Percent Infection
PI= ( Ad / At)*100
Disease Grading
Stop
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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5. B. Disease Detection Process
Figure4: Disease detection Process
V. RESULTS
A. Image Acquisition
Figure 5 shows the images of pomegranate leaf diseased by
Bacterial Blight.
Sample 1
Sample 2
Sample 3
Figure5: samples of Leaf Diseased by Bacterial Blight
Start
Image Acquisition
Image Resize
Image Preprocessing
(Image correction,
Shadow Removal)
Color Image
Segmentation (KMeans
Clustering)
Diseased spot extracted
If Leaf?
Yes
No
If Fruit?
Yellow
margin around
diseased area?
Crack
through
black spot?
Yes
No
No
Bacterial blight on
leaf
Good Leaf /
wrong result
Bacterial blight
on fruit
No
Good fruit /
wrong result
Yes
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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6. B. Image Pre-processing
Resize
The image is resized to a resolution of
[250 300].
Morphology
Operations like erosion and dilation are used. Shadow
removal algorithms are applied to the images wherever
necessary.
C. Colour image segmentation
K-means segmentation algorithm requires users to select
the value ‘k’. The correct choice of k is often ambiguous.
Increasing k will always reduce the amount of error in the
resulting clustering, to the extreme case of zero error if each
data point is considered its own cluster (i.e., when k equals
the number of data points, n). Intuitively then, the optimal
choice of k will strike a balance between maximum
compression of the data using a single cluster, and
maximum accuracy by assigning each data point to its own
cluster.
After some trial and error method, for the current work,
value of K is chosen as 2.
D. Calculating AT and AD
Figure6 shows the binary images of the original resized
image.
Sample1
Sample 2
Sample 3
Figure6: Black and white images of the different query
image
Figure7 shows the images of the diseased portion.
Sample1
Sample 2
Sample 3
Figure 7: Diseased portion of the different query image
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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7. E. Disease grading
From (1), Percent-infection is given by
PI= (AD / AT) *100
= ( 72990/ 178334 ) * 100
= 40.9288 %
Sample 1
Sample 2
Sample 3
Figure8: Grading Results of different Query images
A System is developed for disease grading by referring to
the disease scoring scale in Table1 to grade the disease.
Figure8 shows the result of grading for different images
From the result, it can be observed that the accurate values
of percent-infection and disease grade are obtained with
which a proper treatment advisory can be given thereby
eliminating the above mentioned problems. Also chemical
spray frequency can be minimised thereby reducing
chemical residue in plant parts i.e. food or fodder parts.
F. Disease Detection Results:
Edge Detection algorithms have been used along with black
spot detection algorithm.
Figure9: Leaf Disease Detection for un-diseased leaf image
Figure10: Leaf Disease Detection for diseased leaf image
Figure11: fruit disease identification for un-diseased fruit
image
Figure12: fruit disease identification for diseased fruit
image
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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