Design of Quad copter for Agricultural ApplicationIRJET Journal
This document describes the design of a quadcopter for agricultural applications such as detecting plant diseases and anomalies. The quadcopter would use digital image processing techniques to capture images of crops and identify issues. This would help farmers increase yields by precisely monitoring crop health with limited resources. Specifically, the quadcopter would help detect two common cotton diseases - leaf spot and reddening disease - by identifying their characteristic symptoms in images. The automated detection process could save farmers time and costs compared to manual scouting. Overall, the quadcopter aims to help revolutionize precision agriculture through innovative monitoring solutions.
Remote sensing and GIS techniques can provide timely and accurate information for agricultural monitoring over large areas. Remote sensing uses sensors aboard satellites or drones to capture electromagnetic radiation reflected or emitted from crops and soil. GIS allows integration of spatial data for analysis. Applications include crop identification and acreage estimation, growth monitoring, soil moisture and fertility assessment, pest and disease detection, and yield estimation. Various sensors such as multispectral, thermal, LIDAR and hyperspectral are used to analyze vegetation, soil properties, and assess crop health. Drones equipped with different sensors can assist with crop scouting, inventory management and precision agriculture.
Precision farming uses remote sensing and data analysis to optimize crop yields. It involves observing field variations in crops and soil properties to determine management needs. Precision farming can increase productivity while reducing environmental impacts through efficient use of land, water, and agrochemicals. However, challenges remain in implementing precision farming technologies in some countries due to lack of expertise, high costs, and poor infrastructure.
Processed based crop models try to mimic how plants respond to their environment. This presentation discusses using the DSSAT family of crop models to project yields under different climate scenarios for use in global economic modeling. The models require inputs for weather, soil properties, variety attributes and more. They output daily growth metrics and end of season yields. By running the models repeatedly across locations, global yield projections can be generated to feed into models like IMPACT that assess impacts at a regional level. Data quality, model robustness, and computational efficiency are important considerations.
This document discusses a project to integrate agricultural information systems into an early warning system for climate change adaptation and mitigation in Uganda. The objectives are to strengthen NARO's capacity to develop adaptation and mitigation interventions, develop policy recommendations, and build local climate risk management capacity. Activities include evaluating the "aWhere" agroclimatic information system for short-term weather and pest forecasting and integrating existing agricultural data into an early warning system. Deliverables will include a climate database, an interactive online system, and evaluation reports. Expected outcomes are forecasts of climate variability trends, impacts on agriculture, and recommendations for research, policy, and decision making.
Precision agriculture (PA) / Satellite farming (SF) or Site specific crop management (SSCM) is based on: observing, measuring and responding to inter and intra-field variability in crops. Learn More.
The document discusses the application of geospatial technologies in agriculture. It provides examples of how remote sensing, GIS, and GPS technologies can be used to map soil variability, detect crop health issues, monitor pests and diseases, and enable precision farming. These tools provide spatial data and analysis that can improve decision making around irrigation, fertilizer application, pest management, and more. When integrated, geospatial technologies provide valuable information to farmers and agricultural managers.
Design of Quad copter for Agricultural ApplicationIRJET Journal
This document describes the design of a quadcopter for agricultural applications such as detecting plant diseases and anomalies. The quadcopter would use digital image processing techniques to capture images of crops and identify issues. This would help farmers increase yields by precisely monitoring crop health with limited resources. Specifically, the quadcopter would help detect two common cotton diseases - leaf spot and reddening disease - by identifying their characteristic symptoms in images. The automated detection process could save farmers time and costs compared to manual scouting. Overall, the quadcopter aims to help revolutionize precision agriculture through innovative monitoring solutions.
Remote sensing and GIS techniques can provide timely and accurate information for agricultural monitoring over large areas. Remote sensing uses sensors aboard satellites or drones to capture electromagnetic radiation reflected or emitted from crops and soil. GIS allows integration of spatial data for analysis. Applications include crop identification and acreage estimation, growth monitoring, soil moisture and fertility assessment, pest and disease detection, and yield estimation. Various sensors such as multispectral, thermal, LIDAR and hyperspectral are used to analyze vegetation, soil properties, and assess crop health. Drones equipped with different sensors can assist with crop scouting, inventory management and precision agriculture.
Precision farming uses remote sensing and data analysis to optimize crop yields. It involves observing field variations in crops and soil properties to determine management needs. Precision farming can increase productivity while reducing environmental impacts through efficient use of land, water, and agrochemicals. However, challenges remain in implementing precision farming technologies in some countries due to lack of expertise, high costs, and poor infrastructure.
Processed based crop models try to mimic how plants respond to their environment. This presentation discusses using the DSSAT family of crop models to project yields under different climate scenarios for use in global economic modeling. The models require inputs for weather, soil properties, variety attributes and more. They output daily growth metrics and end of season yields. By running the models repeatedly across locations, global yield projections can be generated to feed into models like IMPACT that assess impacts at a regional level. Data quality, model robustness, and computational efficiency are important considerations.
This document discusses a project to integrate agricultural information systems into an early warning system for climate change adaptation and mitigation in Uganda. The objectives are to strengthen NARO's capacity to develop adaptation and mitigation interventions, develop policy recommendations, and build local climate risk management capacity. Activities include evaluating the "aWhere" agroclimatic information system for short-term weather and pest forecasting and integrating existing agricultural data into an early warning system. Deliverables will include a climate database, an interactive online system, and evaluation reports. Expected outcomes are forecasts of climate variability trends, impacts on agriculture, and recommendations for research, policy, and decision making.
Precision agriculture (PA) / Satellite farming (SF) or Site specific crop management (SSCM) is based on: observing, measuring and responding to inter and intra-field variability in crops. Learn More.
The document discusses the application of geospatial technologies in agriculture. It provides examples of how remote sensing, GIS, and GPS technologies can be used to map soil variability, detect crop health issues, monitor pests and diseases, and enable precision farming. These tools provide spatial data and analysis that can improve decision making around irrigation, fertilizer application, pest management, and more. When integrated, geospatial technologies provide valuable information to farmers and agricultural managers.
Varieties with diverse maturity class,
Striga and drought-tolerant maize varieties
Soil fertility management technologies
Good agronomic practices e.g. planting dates
The document discusses the need for a systemic view of food security that integrates data from local to global scales for early warning analysis. It outlines various factors that influence food security at local, national, regional and global levels. It then describes the FEWS NET Data Warehouse's compilation of sub-national agricultural statistics from over 165 countries, totaling over 3 million data points on area, yield and production for thousands of crops. The document explains that linking these statistical data to shapefiles that track the evolution of reporting units over time will allow for more accurate agricultural production estimates and analyses using remote sensing data.
This document discusses precision agriculture and provides an overview of key concepts:
1. Precision agriculture aims to optimize field management to match crop needs, protect the environment, and boost farm economics through efficient practices.
2. It involves characterizing field variability, making decisions based on soil maps and sensor data, and implementing variable-rate technology.
3. Current trends include high-accuracy GPS, input management like variable-rate fertilizer application, and information management tools to aid decision-making.
4. The document describes technologies like guidance systems, drones, wireless sensors, and yield mapping that are part of precision agriculture approaches.
The document outlines a framework for measuring and monitoring land health and degradation through field measurements and remote sensing. It describes a stratified sampling approach with sentinel sites, clusters, plots and sub-plots to collect biophysical data on vegetation, soils, and trace gases. Remote sensing is used to extrapolate ground measurements and characterize spatial heterogeneity. Soil and carbon models integrate field and remote sensing data for carbon accounting and analysis at local to global scales.
Vital Signs: An integrated monitoring system for agricultural landscapesafrica-rising
Presented by Roseline Remans, Columbia University at the Africa RISING–CSISA Joint Monitoring and Evaluation Meeting, Addis Ababa, Ethiopia, 11-13 November 2013
Weed Sensing SPAA Precision Agriculture Factsheet 2016
SPAA is a non-profit independent membership based group formed in 2002 to promote precision agriculture in Australia. www.spaa.com.au Twitter: SPAA_EO, SPAA_DO
Brazil uses remote sensing satellite data to monitor deforestation, forest degradation, and logging in the Amazon biome. Each year, gross deforestation and degradation rates are published. Preliminary data on large deforestation events are disseminated every 15 days online. The main elements of Brazil's monitoring system are the TERRA-AMAZON software platform, PRODES project for Amazon deforestation monitoring, DETER for near real-time deforestation detection using MODIS, and DEGRAD for degradation monitoring. FAO aims to provide training to other countries so each can develop their own autonomous satellite forest monitoring systems.
Dr. B. L. Sinha discusses the history and definition of precision agriculture. Precision agriculture has been practiced for hundreds of years through adaptations like the transition from horse-drawn plows to tractors. In recent decades, technology like GPS, GIS systems, and remote sensing has allowed for more precise data collection and analysis at subfield levels. This enables variable applications tailored to spatial and temporal variability in fields. By improving efficiency and reducing waste, precision agriculture benefits farmers through increased profits and more sustainable practices.
precise weed management is very useful under large land holdings which reduces cost of cultivation to a greater extent. remote sensing plays a major role in site specific weed management
Precision Farming helps findout nutrient and micro nutrient deficiency in minute areas of soils and enables application of nutrients/micro nutrients in the soil where deficiency exists. This saves money and helps soil improvement.
Remote Sensing of Wheat Rusts - A dream or reality?CIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Precision Agriculture: a concise introduction Joseph Dwumoh
The presentation supplies a brief introduction to what precision agriculture is, what drives its adoption, and what challenges the acceptance of the technologies involved.
Geospatial tools for evidence-based targetingafrica-rising
Presentation by Francis Muthoni regarding progress in 2017/18 with application of GIS tools in targeted scaling under the Africa RISING - NAFAKA project. This presentation was made at the the annual review and planning meeting for the Africa RISING - NAFAKA project on 26 - 27 June 2018.
This document discusses the use of agricultural drones and their various sensor technologies. It describes how visual, multispectral, thermal, LIDAR and hyperspectral sensors can be used for tasks like aerial mapping, plant health monitoring, livestock detection and precision agriculture. Examples are given of drone applications like chemical spraying, crop scouting and inventory management. The document also notes challenges for agricultural drones, such as limited battery life and the need for reliable data networks to download drone images and videos.
Reference Strips and Precision Sensors for Nitrogen Managementuiolgawalsh
This document summarizes a presentation on precision agriculture given by Olga Walsh. The presentation covered the benefits of precision agriculture for producers through tools like variable rate technology, current Idaho research projects on improving water and nitrogen use efficiency in wheat, and the future of precision agriculture with technologies like drones and robotics. Walsh discussed the concepts of yield goal versus yield potential and how crop sensors can help estimate yield potential to determine precise nitrogen needs. Reference strips and understanding common misconceptions about sensors were also covered.
An Introduction to Data Fusion for Ethiopian Agricultural Research: Incorpora...essp2
An Introduction to Data Fusion for Ethiopian Agricultural Research: Incorporating Remote Sensing, GIS, and Spatial Estimation Techniques for Enhanced Analysis
Precision agriculture uses information technology to optimize crop and soil management down to sub-field levels. It relies on precision agriculture technologies like GPS, weather stations, remote sensing, digital elevation models, soil surveys, databases, and GIS to collect and analyze spatial data on variables like soil type, temperature and moisture. This data is used to identify management zones and strategically implement practices that ensure crops and soil receive exactly what they need to maximize health and productivity.
Global positioning system (gps) and its application in precision farmingDr. M. Kumaresan Hort.
Global positioning system (GPS) uses satellites to provide precise location data that allows farmers to navigate fields and apply inputs like fertilizer and pesticides exactly where needed. GPS enables variable rate technology to regulate application rates, reducing over-application and costs while minimizing environmental impact. GPS also helps map field boundaries, problem areas, and soil sample locations for variable rate treatment and improved farm management over time.
Precision Farming and Good Agricultural Practices (1).pptxNaveen Prasath
Precision agriculture (PA), as the name implies, refers to the application of precise and correct amounts of inputs like water, fertilizers, pesticides etc. at the correct time to the crop for increasing its productivity and maximizing its yields. The use of inputs (i.e. chemical fertilizers and pesticides) based on the right quantity, at the right time and in the right place.
This type of management is commonly known as “Site-Specific management”
Strictly based on Global Positioning System (GPS) i.e. unique character is precise in time and space.
Varieties with diverse maturity class,
Striga and drought-tolerant maize varieties
Soil fertility management technologies
Good agronomic practices e.g. planting dates
The document discusses the need for a systemic view of food security that integrates data from local to global scales for early warning analysis. It outlines various factors that influence food security at local, national, regional and global levels. It then describes the FEWS NET Data Warehouse's compilation of sub-national agricultural statistics from over 165 countries, totaling over 3 million data points on area, yield and production for thousands of crops. The document explains that linking these statistical data to shapefiles that track the evolution of reporting units over time will allow for more accurate agricultural production estimates and analyses using remote sensing data.
This document discusses precision agriculture and provides an overview of key concepts:
1. Precision agriculture aims to optimize field management to match crop needs, protect the environment, and boost farm economics through efficient practices.
2. It involves characterizing field variability, making decisions based on soil maps and sensor data, and implementing variable-rate technology.
3. Current trends include high-accuracy GPS, input management like variable-rate fertilizer application, and information management tools to aid decision-making.
4. The document describes technologies like guidance systems, drones, wireless sensors, and yield mapping that are part of precision agriculture approaches.
The document outlines a framework for measuring and monitoring land health and degradation through field measurements and remote sensing. It describes a stratified sampling approach with sentinel sites, clusters, plots and sub-plots to collect biophysical data on vegetation, soils, and trace gases. Remote sensing is used to extrapolate ground measurements and characterize spatial heterogeneity. Soil and carbon models integrate field and remote sensing data for carbon accounting and analysis at local to global scales.
Vital Signs: An integrated monitoring system for agricultural landscapesafrica-rising
Presented by Roseline Remans, Columbia University at the Africa RISING–CSISA Joint Monitoring and Evaluation Meeting, Addis Ababa, Ethiopia, 11-13 November 2013
Weed Sensing SPAA Precision Agriculture Factsheet 2016
SPAA is a non-profit independent membership based group formed in 2002 to promote precision agriculture in Australia. www.spaa.com.au Twitter: SPAA_EO, SPAA_DO
Brazil uses remote sensing satellite data to monitor deforestation, forest degradation, and logging in the Amazon biome. Each year, gross deforestation and degradation rates are published. Preliminary data on large deforestation events are disseminated every 15 days online. The main elements of Brazil's monitoring system are the TERRA-AMAZON software platform, PRODES project for Amazon deforestation monitoring, DETER for near real-time deforestation detection using MODIS, and DEGRAD for degradation monitoring. FAO aims to provide training to other countries so each can develop their own autonomous satellite forest monitoring systems.
Dr. B. L. Sinha discusses the history and definition of precision agriculture. Precision agriculture has been practiced for hundreds of years through adaptations like the transition from horse-drawn plows to tractors. In recent decades, technology like GPS, GIS systems, and remote sensing has allowed for more precise data collection and analysis at subfield levels. This enables variable applications tailored to spatial and temporal variability in fields. By improving efficiency and reducing waste, precision agriculture benefits farmers through increased profits and more sustainable practices.
precise weed management is very useful under large land holdings which reduces cost of cultivation to a greater extent. remote sensing plays a major role in site specific weed management
Precision Farming helps findout nutrient and micro nutrient deficiency in minute areas of soils and enables application of nutrients/micro nutrients in the soil where deficiency exists. This saves money and helps soil improvement.
Remote Sensing of Wheat Rusts - A dream or reality?CIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Precision Agriculture: a concise introduction Joseph Dwumoh
The presentation supplies a brief introduction to what precision agriculture is, what drives its adoption, and what challenges the acceptance of the technologies involved.
Geospatial tools for evidence-based targetingafrica-rising
Presentation by Francis Muthoni regarding progress in 2017/18 with application of GIS tools in targeted scaling under the Africa RISING - NAFAKA project. This presentation was made at the the annual review and planning meeting for the Africa RISING - NAFAKA project on 26 - 27 June 2018.
This document discusses the use of agricultural drones and their various sensor technologies. It describes how visual, multispectral, thermal, LIDAR and hyperspectral sensors can be used for tasks like aerial mapping, plant health monitoring, livestock detection and precision agriculture. Examples are given of drone applications like chemical spraying, crop scouting and inventory management. The document also notes challenges for agricultural drones, such as limited battery life and the need for reliable data networks to download drone images and videos.
Reference Strips and Precision Sensors for Nitrogen Managementuiolgawalsh
This document summarizes a presentation on precision agriculture given by Olga Walsh. The presentation covered the benefits of precision agriculture for producers through tools like variable rate technology, current Idaho research projects on improving water and nitrogen use efficiency in wheat, and the future of precision agriculture with technologies like drones and robotics. Walsh discussed the concepts of yield goal versus yield potential and how crop sensors can help estimate yield potential to determine precise nitrogen needs. Reference strips and understanding common misconceptions about sensors were also covered.
An Introduction to Data Fusion for Ethiopian Agricultural Research: Incorpora...essp2
An Introduction to Data Fusion for Ethiopian Agricultural Research: Incorporating Remote Sensing, GIS, and Spatial Estimation Techniques for Enhanced Analysis
Precision agriculture uses information technology to optimize crop and soil management down to sub-field levels. It relies on precision agriculture technologies like GPS, weather stations, remote sensing, digital elevation models, soil surveys, databases, and GIS to collect and analyze spatial data on variables like soil type, temperature and moisture. This data is used to identify management zones and strategically implement practices that ensure crops and soil receive exactly what they need to maximize health and productivity.
Global positioning system (gps) and its application in precision farmingDr. M. Kumaresan Hort.
Global positioning system (GPS) uses satellites to provide precise location data that allows farmers to navigate fields and apply inputs like fertilizer and pesticides exactly where needed. GPS enables variable rate technology to regulate application rates, reducing over-application and costs while minimizing environmental impact. GPS also helps map field boundaries, problem areas, and soil sample locations for variable rate treatment and improved farm management over time.
Precision Farming and Good Agricultural Practices (1).pptxNaveen Prasath
Precision agriculture (PA), as the name implies, refers to the application of precise and correct amounts of inputs like water, fertilizers, pesticides etc. at the correct time to the crop for increasing its productivity and maximizing its yields. The use of inputs (i.e. chemical fertilizers and pesticides) based on the right quantity, at the right time and in the right place.
This type of management is commonly known as “Site-Specific management”
Strictly based on Global Positioning System (GPS) i.e. unique character is precise in time and space.
The African Network for Soil Biology and Fertility (AfNet)CIAT
1. The document discusses yield gaps in African agriculture, where potential yields are much higher than actual yields achieved by farmers. Half the yield gap can be closed through improved soil nutrients and practices, with the other half requiring improved seeds and development policies.
2. The African Network for Soil Biology and Fertility (AfNet) aims to bridge this gap through testing and promoting better technologies and sustainable soil fertility practices. AfNet's strategy is in line with Integrated Soil Fertility Management and improving eco-efficiency in agriculture.
3. AfNet conducts research across Africa, strengthens capacities, and disseminates information to contribute to sustainable soil management and improved welfare of farming communities. It also seeks
GIS generated recommendation domains for scaling crop varieties in Tanzaniaafrica-rising
Poster prepared by Francis Kamau Muthoni, Haroon Sseguya, Mateete Bekunda and Irmgard Hoeschle-Zeledon for the Africa RISING Humidtropics Systems Research Marketplace, Ibadan, Nigeria, 15-17 November 2016
Precision agriculture in maize-based cropping systemsCIMMYT
Precision agriculture aims to ensure crops and soil receive exactly what they need through information technology. It can benefit the environment and farm profits by better using resources like nutrients, water, and pesticides in a spatially and temporally targeted way. Key technologies enabling precision agriculture include GPS, earth observation satellites, drones, proximal sensors, and ICT. These allow for remote sensing, variable rate application, and decision support. Precision agriculture adapted for smallholders in developing countries must address intra-farm variability and be implemented through affordable, appropriate technologies delivered via mobile apps or other ICT to optimize resource use at multiple scales.
IRJET - Agrotech: Soil Analysis and Crop PredictionIRJET Journal
This document presents a system for soil analysis and crop prediction using data mining techniques. The system measures soil parameters like pH, nitrogen, phosphorus and potassium using sensors. It then uses a decision tree algorithm to classify the soil and predict suitable crops. The pH value is used to estimate other nutrient values. The nutrient values and soil type are sent over WiFi to a server, which uses machine learning to predict crops and provide fertilizer recommendations to the farmer. The proposed system automates the soil testing process and aims to help farmers select optimal crops and increase agricultural yields.
Precision farming refers to applying agricultural inputs precisely based on soil, weather, and crop needs to improve productivity, quality, and profitability. It uses technologies like GPS, GIS, remote sensing, and drones to vary application of inputs within single fields based on data collected. This allows for more efficient use of resources like water, fertilizer, and pesticides, increasing yields while reducing environmental pollution. Precision farming is still developing in India but shows potential to significantly increase crop productivity through techniques tailored for India's agricultural conditions and small landholdings.
The document summarizes the work of the Tropical Soil Biology and Fertility Institute of CIAT (TSBF) on Integrated Soil Fertility Management (ISFM). It discusses TSBF's goals, definition of ISFM, impact zones, activities, progress against outputs in different crop systems, collaboration with CRPs, statistics on funding and challenges, and future plans. The overall aim is to improve livelihoods in sub-Saharan Africa through sustainable agricultural production systems based on ISFM principles.
1. The document discusses the development of a machine learning-based system to provide precise crop yield recommendations to farmers in India.
2. Over 60% of Indians work in agriculture but farmers often grow the same crops without trying new varieties and apply fertilizers inconsistently, affecting yields and soil quality.
3. The proposed system aims to address these issues by recommending the optimal crop for a given plot of land based on soil composition and environmental factors using machine learning algorithms.
Credit Seminar:Adoption Of Precision Agriculture In Indian Scenario: It's Sco...Sundeepreddyavula
Precision agriculture refers to applying agricultural inputs precisely based on soil, weather, and crop needs to improve productivity, quality, and profitability. It uses technologies like remote sensing, GPS, and GIS to enable more efficient use of inputs like pesticides, fertilizers, tillage, and irrigation water, bringing higher yields and quality without pollution. While precision agriculture is still nascent in India, studies show it can increase yields 2-3 times through proper soil testing and fertilizer application. Some Indian states and companies are piloting precision agriculture approaches tailored to India's socioeconomic conditions to evaluate yield increases and cost reductions compared to conventional farming. Widespread adoption in India will require overcoming educational, economic, and infrastructure challenges.
Agriculture machinery plays a significant role to enhance the productivity.
Geo-informatics is the science that gather data regarding field conditions (Accurately). These are computational model cum strong algorithm based machinery or equipment to obtain real time data with precise application
This document discusses precision farming and its benefits. Precision farming uses tools like GPS, sensors, and GIS to precisely vary the application of inputs like water, fertilizer and pesticides based on site-specific needs. This improves yields and quality while reducing costs, waste, and environmental impact. Adopting precision farming techniques could help increase yields by 39-150% for crops like tomatoes, chillies, capsicum, brinjal and bhindi. Precision farming also improves farm incomes and makes agriculture more sustainable and environmentally friendly. Widespread adoption will require collaboration between farmers, scientists, engineers and industry to develop the necessary technologies and equipment.
CIMMYT smallholder’s mechanization strategy for sustainable intensification a...africa-rising
Presented by Rabé Yahaya, Walter Mupangwa, Ephrem Tadesse and Frédéric Baudron (CIMMYT) at the SAIRLA Second National Learning Alliance Workshop, ILRI, Addis Ababa, 23 November 2017
Crop yield prediction using data mining techniques.pdfssuserb22f5a
Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.
The document discusses how geo-spatial technologies like remote sensing, GPS, and GIS can help address challenges in the agriculture sector by enabling climate-smart agriculture and precision farming. It provides examples of how these technologies are being used in New Zealand and Malawi to improve crop yields, reduce input costs, and evaluate climate-smart interventions. While India has capabilities in geo-spatial technologies, the document notes there remains scope to increase their application in agriculture to boost productivity and sustainability.
Use of Drones in Rice Production and its effectsRegzBraceros
This document discusses how drone technology can help address challenges in traditional rice farming methods. It provides an overview of how drones can enhance efficiency, optimize resource use, and allow for precision monitoring of rice crops. Drones equipped with cameras and sensors can collect remote sensing data for analysis of field conditions and yield estimates. This data integration has the potential to increase yields while reducing costs and improving sustainability of rice production. The document also examines regulatory considerations, economic viability, and future applications of drone technology in transforming the rice farming industry.
Similar to Developing technology extrapolation domains for agronomic technology packages (20)
Africa RISING project implementation and contribution in Ethiopia. Presented at Africa RISING close-out event.
24-25 January 2023
ILRI campus- Addis Ababa, Ethiopia
The document summarizes a field visit by Africa RISING CGIAR partners to sites in Ethiopia where they are implementing their new SI-MFS initiative. It describes some innovative farmers in the Lemo and Doyogena districts who have adopted integrated crop-livestock-NRM practices promoted by Africa RISING, including using protein-rich legume fodder trees, energy-rich grasses, and soil and water conservation practices. It also highlights the challenges of water shortage and disease, and the potential for the new SI-MFS initiative to build on the success stories and learning from Africa RISING farmers.
This document summarizes planned and ongoing agricultural research activities and studies in the Ethiopian highlands for 2022. It discusses field activities related to livestock feed and forage development as well as crop varietal selection. It also outlines planned, ongoing, and completed studies on topics like gender and scaling assessments. The document notes legacy products to be developed and capacity building efforts. It describes plans to broadcast livestock innovations through local radio and concludes with noting the planned closure of the Africa Research project in Ethiopia in early 2023.
Haimanot Seifu provided a communications update on the Africa RISING program in the Ethiopian Highlands. Key activities before the program ends this year include producing extension manuals, policy briefs, a special journal issue, and a photo book. Surveys are also ongoing regarding gender, monitoring impacts, spillover effects, and scaling. Africa RISING is partnering with AICCRA on workshops, surveys, training modules, and broadcasting feed and forage technologies on local radio stations. A new initiative called SI-MFS involving mixed farming systems in 6 countries was also launched in May to run initially for 3 years from 2022-2024. Support is needed from CKM for legacy products, facilitating
Technique de compostage des tiges de cotonnier au Mali-Sudafrica-rising
Poster prepared by Moumini Guindo, Bouba Traoré, Birhanu Zemadim Birhanu, and Alou Coulibaly for the 13th Symposium of the Malian Society of Applied Sciences (MSAS), 01 July – 05 August 2022.
Flux des nutriments (N, P, K) des resources organiques dans les exploitations...africa-rising
Poster prepared by Moumini Guindo, Bouba Traoré, Birhanu Zemadim Birhanu, and Alou Coulibaly for the 13th Symposium of the Malian Society of Applied Sciences (MSAS), 01 July 1 – 05 August 2022.
The Africa RISING project in Ethiopia's highlands had the goals of improving food security, gender equality, nutrition, income, and capacity building through sustainable intensification research from 2012-2022. It worked in four regions, implementing tested interventions like improved crops, fertilizers, and mechanization. Over 360,000 households directly benefited from validated technologies in phase two, while over 30,000 people participated in training. The project supported graduate students, published research, and faced challenges like COVID-19 and funding issues before planning its exit strategies.
Eliciting willingness to pay for quality maize and beans: Evidence from exper...africa-rising
Poster prepared by Julius Manda, Adane Tufa, Christopher Mutungi, Arega Alene, Victor Manyong and Tahirou Abdoulaye for the IITA Social Science Group Virtual Meeting, 7 December 2021.
The woman has no right to sell livestock: The role of gender norms in Norther...africa-rising
Presented by Kipo Jimah and Gundula Fischer (IITA) at the virtual conference on Cultivating Equality: Advancing Gender Research in Agriculture and Food Systems, 12-15 October 2021
This document summarizes two assessments conducted by Africa RISING on sustainable intensification and return on investment from 2011-2020. It finds that:
1) The total value of direct benefits to farmers was $74.6 million, while the total project cost was $15.9 million, resulting in a return on investment of 469%.
2) An assessment of progress towards sustainable intensification analyzed households by total production per hectare and compared indicators across five domains. It found that more intensified households showed improved scores in agricultural production, economics, environment, human welfare, and social indicators.
3) A focus on assessments at the woreda (district) level provided insights into differences between communities and guidance for
The document summarizes the results of a nutrition assessment study and lessons learned from it. The study aimed to identify how Africa RISING interventions contributed to household nutrition. It used a qualitative research approach with key informant interviews and focus group discussions in Ethiopia. The results showed that the interventions helped to produce and consume a more diverse and nutritious diet, generate income, and improve knowledge of food production and preparation. However, diet diversity remained low and certain nutrient-rich foods were still limited. Key lessons were that technical nutrition support needs frequent follow-ups, and engaging community leaders and husbands is important for influencing mothers' nutrition practices.
The document discusses plans for scaling assessment of Africa RISING interventions. It notes that Africa RISING's second phase focused on scaling approaches through recruiting scaling partners, training of trainers, multi-stakeholder meetings, and research backstopping. The assessment aims to document scaling practices, identify areas for increased support, and develop an exit strategy as the program period concludes. It will use ILRI's scaling framework over six months to provide a technical report and scientific paper.
This document summarizes a presentation on conducting on-farm trials at scale using crowdsourcing. It discusses the benefits and challenges of traditional on-farm trials, and proposes a solution using digital platforms and farmer participation. Farmers would receive random combinations of varieties to test on their own farms and provide rankings. Data would be collected and analyzed to provide feedback to farmers. The approach aims to increase representation while reducing costs compared to traditional on-farm trials. It outlines 10 steps for implementation, including defining varieties, designing projects, recruiting farmers, preparing packages, data collection, analysis and discussion.
Contribution of Africa RISING validated technologies, nutrition-education interventions to household nutrition and participatory nutrition-education need assessment with seasonal food availability in Amhara, Oromia and SNNP regions of Ethiopia
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Developing technology extrapolation domains for agronomic technology packages
1. Developing technology extrapolation
domains for agronomic technology packages
Francis Muthoni
International Institute of Tropical Agriculture (IITA)
Africa RISING ESA Project review and planning meeting
11 – 12 September 2019, Dar es Salaam, Tanzania
2. Outline
Example of extrapolation domains for AR technologies
Data requirements for generating domains
Overview of proposed work-plan
3. Why generate extrapolation domains?
Different SI technology options are suited for specific biophysical
context
Proper spatial targeting of SI technologies is required to:
Reduce risk of failure
Maximize impact
Increase probability of adoption
Rationalise investment of limited resources
5. ESI for maize fertilizer packages in Tanzania
Lower ESI value indicates high similarity with conditions in reference
trial sites
Less risk for extrapolating a particular package of technologies
Muthoni et al 2019, GeoCarto Int., 34(4), 368 - 390
6. Data requirements from technology trials
Availability of data from technology validation sites determines the
method & practicality of domains to be generated
GPS location: Lat., Long., Altitude
Crop varieties
Inorganic fertilizers rates : N P rates
Organic amendments: manure, crop residues
Soil water conservation practices: Tied ridges, fanya juu/Chini
Conservation Agri. practices: No tillage, minimum tillage….
Grain/biomass yields
Economics: Profitability
8. Activity 1.3.1.1: Extrapolation domains for CA
technologies
A multivariate random Forests (MRF) model will be used to predict
grain yields for maize varieties grown with different CA practices
Hengl et al., 2017
12. Africa Research in Sustainable Intensification for the Next Generation
africa-rising.net
This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
Thank You