The document provides an overview of Team Newton's proposed predictive farming model. It includes sections on factors that impact farmers' incomes in India like education, farm size, technology, and infrastructure. The model would collect live and historical data on soil, weather, markets and more to predict crop yields and advise farmers. It outlines data collection techniques using sensors on drones, balloons and more. Key performance indicators focus on income increases and reduced crop damage. The plan details increasing farmer involvement, expanding operations over 10 years, and introducing a tablet device called Krishi Grantha for individual analysis. Total projected costs are over 500 crore rupees for 10 years.
FOUNDATION
DETAILS OF PROGRAMME
FORMATION OF GGRC
EVALUATION OF MICRO IRRIGATION SYSTEM
ACHIEVEMENTS OF GGRC
NECCESITY OF GGRC
FEATURES OF GGRC
BENEFITS OF GGRC
Farm mechanization can significantly benefit Indian agriculture by increasing productivity and efficiency while reducing costs. The government has implemented various schemes to promote mechanization, but challenges remain around small landholdings, equipment costs and quality, and financing. To further increase mechanization, India can develop custom hiring centers, use policies like Make in India and CSR funds, and provide clearer guidelines and support for the agricultural equipment industry and farmers. Overcoming obstacles can help India achieve its goal of transforming agriculture through increased mechanization.
This document summarizes the strategic plan to double farmer income in India by 2022. It outlines that the Prime Minister and Finance Minister have made doubling farmer income a priority. It will require annual growth of 14.86% over the next 5-6 years. The plan includes seven strategies focusing on irrigation, seeds, warehousing, food processing, markets, insurance, and ancillary activities. Past strategies focused on output, but did not consider income. Multiple sources will contribute to income growth, including productivity, diversification, and non-farm activities. National programs and stakeholder consultation are part of the approach. Current data shows average farmer income needs to double to meet the goal. Coordinated efforts across states and sectors are needed
This document discusses sustainable agriculture and data management tools. It introduces the Agri-Data Solution, a secure online platform that allows farmers to track sustainability metrics and food security data for over 65 million acres. The platform can monitor metrics like soil conservation practices, nitrogen use efficiency, water and energy use, and integrate additional data like soil tests, yields, pesticides, and irrigation. It provides tools to help farmers and landowners improve sustainability and food production.
The document summarizes the average monthly income of Indian farmers based on a survey. It finds the average to be Rs. 6,426 per month. The main sources of income are cultivation (47%), livestock (13%), non-farm business (8%), and wages (32%). To double their income to Rs. 12,852, agricultural growth would need to increase to 12-14% annually from the current 4.1%. The document then proposes several institutional and policy reforms to stabilize farmer incomes, including establishing agriculture planning bodies, conducting a technology-driven census, providing farmers with identity cards and salaries, and setting minimum income support during adverse conditions.
Kisan Mitra - Transitioning farmer in to a Smart FarmerSai Sharan Beepeta
Kisan Mitra is a service that uses drones, sensors and technology to help transition farmers into smart farming. It surveys large farmlands from the air and inspects individual small farms. This allows them to 1) predict and detect crop diseases, 2) monitor crop cycles efficiently, 3) regularly monitor soil fertility, and 4) assist from sowing to reaping. Kisan Mitra offers a one stop solution for smart farming that can increase yields and productivity while reducing uncertainty through technological support.
The study analyzed the socioeconomic conditions, costs and returns of rice production, and technology adoption levels of 50 rice farmers in Bardhaman district, West Bengal. It found the average landholding was 1.82 hectares, with a cropping intensity of 226.87%. Total production costs and returns varied by farm size and season. Winter rice was more profitable due to higher yields and market demand. Technology adoption levels differed across farm sizes and seasons. The main constraints faced by farmers were lack of institutional credit, poor extension services, and inefficient marketing. The study recommends encouraging winter rice, mechanization, irrigation infrastructure, technology adoption, and improved marketing and extension services to increase farmer profits.
Crop Selection Method Based on Various Environmental Factors Using Machine Le...IRJET Journal
This document proposes two crop selection methods using machine learning:
1. A Crop Selection Method that uses classification algorithms to select the most suitable crop based on environmental and economic factors like temperature, rainfall, soil type, and market prices.
2. A Crop Sequencing Method that uses a crop sequencing algorithm to suggest an optimal sequence of crops over a growing season based on predicted yield rates and market prices to maximize profits. Both methods use a machine learning tool called WEKA and historical crop data to make predictions.
FOUNDATION
DETAILS OF PROGRAMME
FORMATION OF GGRC
EVALUATION OF MICRO IRRIGATION SYSTEM
ACHIEVEMENTS OF GGRC
NECCESITY OF GGRC
FEATURES OF GGRC
BENEFITS OF GGRC
Farm mechanization can significantly benefit Indian agriculture by increasing productivity and efficiency while reducing costs. The government has implemented various schemes to promote mechanization, but challenges remain around small landholdings, equipment costs and quality, and financing. To further increase mechanization, India can develop custom hiring centers, use policies like Make in India and CSR funds, and provide clearer guidelines and support for the agricultural equipment industry and farmers. Overcoming obstacles can help India achieve its goal of transforming agriculture through increased mechanization.
This document summarizes the strategic plan to double farmer income in India by 2022. It outlines that the Prime Minister and Finance Minister have made doubling farmer income a priority. It will require annual growth of 14.86% over the next 5-6 years. The plan includes seven strategies focusing on irrigation, seeds, warehousing, food processing, markets, insurance, and ancillary activities. Past strategies focused on output, but did not consider income. Multiple sources will contribute to income growth, including productivity, diversification, and non-farm activities. National programs and stakeholder consultation are part of the approach. Current data shows average farmer income needs to double to meet the goal. Coordinated efforts across states and sectors are needed
This document discusses sustainable agriculture and data management tools. It introduces the Agri-Data Solution, a secure online platform that allows farmers to track sustainability metrics and food security data for over 65 million acres. The platform can monitor metrics like soil conservation practices, nitrogen use efficiency, water and energy use, and integrate additional data like soil tests, yields, pesticides, and irrigation. It provides tools to help farmers and landowners improve sustainability and food production.
The document summarizes the average monthly income of Indian farmers based on a survey. It finds the average to be Rs. 6,426 per month. The main sources of income are cultivation (47%), livestock (13%), non-farm business (8%), and wages (32%). To double their income to Rs. 12,852, agricultural growth would need to increase to 12-14% annually from the current 4.1%. The document then proposes several institutional and policy reforms to stabilize farmer incomes, including establishing agriculture planning bodies, conducting a technology-driven census, providing farmers with identity cards and salaries, and setting minimum income support during adverse conditions.
Kisan Mitra - Transitioning farmer in to a Smart FarmerSai Sharan Beepeta
Kisan Mitra is a service that uses drones, sensors and technology to help transition farmers into smart farming. It surveys large farmlands from the air and inspects individual small farms. This allows them to 1) predict and detect crop diseases, 2) monitor crop cycles efficiently, 3) regularly monitor soil fertility, and 4) assist from sowing to reaping. Kisan Mitra offers a one stop solution for smart farming that can increase yields and productivity while reducing uncertainty through technological support.
The study analyzed the socioeconomic conditions, costs and returns of rice production, and technology adoption levels of 50 rice farmers in Bardhaman district, West Bengal. It found the average landholding was 1.82 hectares, with a cropping intensity of 226.87%. Total production costs and returns varied by farm size and season. Winter rice was more profitable due to higher yields and market demand. Technology adoption levels differed across farm sizes and seasons. The main constraints faced by farmers were lack of institutional credit, poor extension services, and inefficient marketing. The study recommends encouraging winter rice, mechanization, irrigation infrastructure, technology adoption, and improved marketing and extension services to increase farmer profits.
Crop Selection Method Based on Various Environmental Factors Using Machine Le...IRJET Journal
This document proposes two crop selection methods using machine learning:
1. A Crop Selection Method that uses classification algorithms to select the most suitable crop based on environmental and economic factors like temperature, rainfall, soil type, and market prices.
2. A Crop Sequencing Method that uses a crop sequencing algorithm to suggest an optimal sequence of crops over a growing season based on predicted yield rates and market prices to maximize profits. Both methods use a machine learning tool called WEKA and historical crop data to make predictions.
Variable rate fertilizer technology allows farmers to precisely apply fertilizer based on soil needs within individual fields. This report analyzes the feasibility and benefits of using variable rate fertilizer. It finds that the technology saves farmers money by reducing over-application of fertilizer and boosting crop yields. The report recommends that farmers incorporate variable rate technology to lower costs per acre and remain competitive in today's agriculture industry.
This document summarizes the key topics and findings from the book "Agricultural Transformation in Nepal: Trends, Prospects and Policy Options". It discusses Nepal's agricultural sector challenges including lower and fluctuating growth, declining productivity, and rising imports. However, it also notes prospects like shifting diets driving demand, commercialization, and emerging value chains. The way forward involves ensuring food security through technology adoption, increasing public and private investment, promoting diversification, and developing domestic and regional value chains. Strengthening infrastructure, quality standards, contract farming and trade opportunities can help realize the agriculture sector's potential.
Uruguay uses detailed livestock data and statistics to calculate greenhouse gas emissions from agriculture. They are moving from a basic Tier 1 method to a more complex Tier 2 approach that considers animal category, weight, diet, and production system. This requires dynamic activity data on topics like animal numbers, weights, reproduction rates, and feed composition over time. Challenges include improving national statistics to provide this level of detail and conducting more country-level research on diet and performance parameters. Regional collaborations could help speed progress by linking science and policy across countries.
The document summarizes Ethiopia's agricultural extension efforts from 1953 to the present. It describes 3 phases: 1) 1953-1974 under the Imperial regime focusing on demonstration and training, 2) 1974-1991 under military rule expanding minimum package programs, and 3) post-1991 under the current government adopting a participatory approach called PADETES. Key aspects included establishing agricultural colleges, deploying development agents, introducing improved practices, expanding farmer participation, and increasing crop yields through new extension strategies.
IFPRI South Asia researchers Devesh Roy, Ruchira Boss, Mamata Pradhan and Manmeet Ajmani presented ‘Understanding the landscape of pulse policy in India and implications for trade’ to the Global Pulse Federation. The paper examines Indian policy around production, consumption and trade. The need for pulse trade policy in India to be supportive of Domestic priorities focused on serving interest of both India’s farmers and consumers.
The document describes Agricultural Integrated Surveys (AGRIS), a new survey program designed by FAO to provide more timely and relevant agricultural data. AGRIS uses a modular approach with a core annual survey and rotating thematic modules to generate data for indicators like SDGs. It provides a cost-effective way to build sustainable rural information systems. Fifteen countries will implement AGRIS with technical and financial support from FAO and partners like the World Bank and donor agencies.
Embedded Decision Support System for Smart FarmingIRJET Journal
1) The document proposes an embedded decision support system using IoT and wireless sensor networks to help farmers monitor soil conditions and make informed decisions.
2) Sensors would measure temperature, humidity, soil moisture and send the data via ZigBee to a database monitoring station and to the IoT platform ThingSpeak.
3) ThingSpeak allows the sensor data to be visualized online, allowing farmers to access it anywhere to track field conditions and receive advice.
Presented at the Pulses for Sustainable Agriculture and Human Health” on 31 May-1 June 2016 at NASC, New Delhi, India. The conference was jointly organised by the International Food Policy Research Institute (IFPRI), National Academy of Agricultural Sciences (NAAS), TCi of Cornell University (TCi-CU) and Agriculture Today.
This document summarizes Canada's experience collecting agricultural land data for its Census of Agriculture. It discusses how Canada collects total land area at the holding level, including breakdowns by land use and tenure. It also describes how Canada is moving to collect more detailed geographic data at the parcel level, leveraging crop insurance and remote sensing data to obtain complete land cover information without direct farmer contact. This helps validate census data and produce accurate crop area estimates.
Crop Yield Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict crop yields. It begins with an abstract that outlines the importance of agriculture and maintaining crop production in India. The objectives are then stated as empowering farmers with knowledge of different crops and climate changes and overcoming obstacles by applying machine learning to predict crop yield based on factors like temperature, rainfall, and area. Related work on using climate data and machine learning algorithms like SVM and regression to predict yields is reviewed. The proposed system aims to select optimal crops for a land plot using techniques like XGBoost, Naive Bayes and SVM based on environmental variables. It is concluded that opportunities remain to enhance outcomes by considering all variables simultaneously and using larger datasets.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Doubling Farmers' Income by 2022 through Data IntelligenceSocialCops
SocialCops has joined the mission to double farmers’ income by 2022 through data intelligence.
The solution deck talks about the problems of the Indian agriculture landscape, how data-driven decision making can revolutionize agriculture in India and presents a case study in which data intelligence was instrumental in driving agrarian reform.
IRJET- Survey on Crop Suggestion using Weather AnalysisIRJET Journal
The document discusses a proposed model to predict the most suitable crop for a given location based on weather analysis and soil parameters. It would use fuzzy logic, Gradient Boosted Decision Tree (GBDT) algorithm, and R Neuralnet Package. The model aims to address the problems of crop failure, food shortage, and increasing farmer suicides by recommending crops suited to the climatic conditions and soil quality of a particular site. It would provide suggestions on both crop yield and suitable crop types to maximize agricultural productivity. The inputs to the system would be meteorological and soil data, and it would analyze past and future weather data to recommend crops.
This document describes a decision support system for farmers that includes modules for disease detection, weather prediction, and market price analysis. The disease detection module uses image processing to identify plant diseases from leaf images and provides treatment suggestions. The weather prediction module forecasts weather conditions to help farmers make cultivation decisions. The market price module analyzes historical price data to predict future market prices so farmers can decide when to sell their harvests. The system was designed with a web portal for farmers and administrators. Algorithms like multiple linear regression were used for weather predictions. Results from the different modules are displayed graphically and textually to aid farmers' decision making. The overall goal is to help farmers increase crop yields and profits through informed agricultural planning and responses to threats
This document presents a paper on applying machine learning algorithms to crop selection for precision agriculture. The paper was presented at the International Conference on Digital Applications, Transformation, and Economy by researchers from COEP Tech. University, Pune and GEC Wayanad, Kerala. The paper introduces the topic, reviews literature on previous crop prediction studies using ML algorithms, outlines the objectives and methodology, presents results comparing the performance of various ML models, and concludes with suggestions for future work. Key results showed the Gaussian Naive Bayes algorithm achieved 99.5% accuracy in crop prediction, outperforming other models evaluated. The study demonstrates the potential of ML to improve crop management and food security through more precise recommendations based on soil and weather conditions.
This document provides an executive summary of DeHaat, an online farmers' marketplace that connects farmers in India to agricultural inputs, advisory services, and markets using a mobile-based technology platform. It outlines DeHaat's solution to improve access to quality agricultural services for 70 million small farmers in India. Key points include:
- DeHaat operates a mobile marketplace that provides farmers with inputs, advisory, and market linkages to increase their income up to 50% by improving efficiency.
- It has grown its farmer network to over 20,000 farmers through local operators and aims to reach 1 million farmers by 2020.
- The company generates revenue through input sales, market linkages, and leveraging farmer data analytics with
The journal publishes original works with practical significance and academic value. Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management. IJMRR is an international forum for research that advances the theory and practice of management. IJMRR is an international forum for research that advances the theory and practice of management. Organizational Behaviour, Rural Marketing, Business & Ethics International, Business & Ethics International, Business & Ethics International. All papers submitted to IJMRR are subject to a double-blind peer review process. All papers submitted to IJMRR are subject to a double-blind peer review process.
Day 3 abu syed, bangladesh centre for advanced studies (bcas), bangladesh, ar...ICIMOD
National meteorology institutions can improve climate services for agriculture by providing higher resolution weather projections and predictions to enable more effective early warning systems. A case study on the use of climate information in potato farming in Bangladesh found that sub-hourly weather data and high-resolution models are needed for crop modeling and early warnings. Current weather forecasts are not accurate enough for this purpose. Involving private sector partners could help disseminate services, increase sales of related products, and provide an embedded advisory service to contract farmers. National needs include improved climate information systems, water management technologies, and institutional support for early warning systems.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
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.
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.
Variable rate fertilizer technology allows farmers to precisely apply fertilizer based on soil needs within individual fields. This report analyzes the feasibility and benefits of using variable rate fertilizer. It finds that the technology saves farmers money by reducing over-application of fertilizer and boosting crop yields. The report recommends that farmers incorporate variable rate technology to lower costs per acre and remain competitive in today's agriculture industry.
This document summarizes the key topics and findings from the book "Agricultural Transformation in Nepal: Trends, Prospects and Policy Options". It discusses Nepal's agricultural sector challenges including lower and fluctuating growth, declining productivity, and rising imports. However, it also notes prospects like shifting diets driving demand, commercialization, and emerging value chains. The way forward involves ensuring food security through technology adoption, increasing public and private investment, promoting diversification, and developing domestic and regional value chains. Strengthening infrastructure, quality standards, contract farming and trade opportunities can help realize the agriculture sector's potential.
Uruguay uses detailed livestock data and statistics to calculate greenhouse gas emissions from agriculture. They are moving from a basic Tier 1 method to a more complex Tier 2 approach that considers animal category, weight, diet, and production system. This requires dynamic activity data on topics like animal numbers, weights, reproduction rates, and feed composition over time. Challenges include improving national statistics to provide this level of detail and conducting more country-level research on diet and performance parameters. Regional collaborations could help speed progress by linking science and policy across countries.
The document summarizes Ethiopia's agricultural extension efforts from 1953 to the present. It describes 3 phases: 1) 1953-1974 under the Imperial regime focusing on demonstration and training, 2) 1974-1991 under military rule expanding minimum package programs, and 3) post-1991 under the current government adopting a participatory approach called PADETES. Key aspects included establishing agricultural colleges, deploying development agents, introducing improved practices, expanding farmer participation, and increasing crop yields through new extension strategies.
IFPRI South Asia researchers Devesh Roy, Ruchira Boss, Mamata Pradhan and Manmeet Ajmani presented ‘Understanding the landscape of pulse policy in India and implications for trade’ to the Global Pulse Federation. The paper examines Indian policy around production, consumption and trade. The need for pulse trade policy in India to be supportive of Domestic priorities focused on serving interest of both India’s farmers and consumers.
The document describes Agricultural Integrated Surveys (AGRIS), a new survey program designed by FAO to provide more timely and relevant agricultural data. AGRIS uses a modular approach with a core annual survey and rotating thematic modules to generate data for indicators like SDGs. It provides a cost-effective way to build sustainable rural information systems. Fifteen countries will implement AGRIS with technical and financial support from FAO and partners like the World Bank and donor agencies.
Embedded Decision Support System for Smart FarmingIRJET Journal
1) The document proposes an embedded decision support system using IoT and wireless sensor networks to help farmers monitor soil conditions and make informed decisions.
2) Sensors would measure temperature, humidity, soil moisture and send the data via ZigBee to a database monitoring station and to the IoT platform ThingSpeak.
3) ThingSpeak allows the sensor data to be visualized online, allowing farmers to access it anywhere to track field conditions and receive advice.
Presented at the Pulses for Sustainable Agriculture and Human Health” on 31 May-1 June 2016 at NASC, New Delhi, India. The conference was jointly organised by the International Food Policy Research Institute (IFPRI), National Academy of Agricultural Sciences (NAAS), TCi of Cornell University (TCi-CU) and Agriculture Today.
This document summarizes Canada's experience collecting agricultural land data for its Census of Agriculture. It discusses how Canada collects total land area at the holding level, including breakdowns by land use and tenure. It also describes how Canada is moving to collect more detailed geographic data at the parcel level, leveraging crop insurance and remote sensing data to obtain complete land cover information without direct farmer contact. This helps validate census data and produce accurate crop area estimates.
Crop Yield Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict crop yields. It begins with an abstract that outlines the importance of agriculture and maintaining crop production in India. The objectives are then stated as empowering farmers with knowledge of different crops and climate changes and overcoming obstacles by applying machine learning to predict crop yield based on factors like temperature, rainfall, and area. Related work on using climate data and machine learning algorithms like SVM and regression to predict yields is reviewed. The proposed system aims to select optimal crops for a land plot using techniques like XGBoost, Naive Bayes and SVM based on environmental variables. It is concluded that opportunities remain to enhance outcomes by considering all variables simultaneously and using larger datasets.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Doubling Farmers' Income by 2022 through Data IntelligenceSocialCops
SocialCops has joined the mission to double farmers’ income by 2022 through data intelligence.
The solution deck talks about the problems of the Indian agriculture landscape, how data-driven decision making can revolutionize agriculture in India and presents a case study in which data intelligence was instrumental in driving agrarian reform.
IRJET- Survey on Crop Suggestion using Weather AnalysisIRJET Journal
The document discusses a proposed model to predict the most suitable crop for a given location based on weather analysis and soil parameters. It would use fuzzy logic, Gradient Boosted Decision Tree (GBDT) algorithm, and R Neuralnet Package. The model aims to address the problems of crop failure, food shortage, and increasing farmer suicides by recommending crops suited to the climatic conditions and soil quality of a particular site. It would provide suggestions on both crop yield and suitable crop types to maximize agricultural productivity. The inputs to the system would be meteorological and soil data, and it would analyze past and future weather data to recommend crops.
This document describes a decision support system for farmers that includes modules for disease detection, weather prediction, and market price analysis. The disease detection module uses image processing to identify plant diseases from leaf images and provides treatment suggestions. The weather prediction module forecasts weather conditions to help farmers make cultivation decisions. The market price module analyzes historical price data to predict future market prices so farmers can decide when to sell their harvests. The system was designed with a web portal for farmers and administrators. Algorithms like multiple linear regression were used for weather predictions. Results from the different modules are displayed graphically and textually to aid farmers' decision making. The overall goal is to help farmers increase crop yields and profits through informed agricultural planning and responses to threats
This document presents a paper on applying machine learning algorithms to crop selection for precision agriculture. The paper was presented at the International Conference on Digital Applications, Transformation, and Economy by researchers from COEP Tech. University, Pune and GEC Wayanad, Kerala. The paper introduces the topic, reviews literature on previous crop prediction studies using ML algorithms, outlines the objectives and methodology, presents results comparing the performance of various ML models, and concludes with suggestions for future work. Key results showed the Gaussian Naive Bayes algorithm achieved 99.5% accuracy in crop prediction, outperforming other models evaluated. The study demonstrates the potential of ML to improve crop management and food security through more precise recommendations based on soil and weather conditions.
This document provides an executive summary of DeHaat, an online farmers' marketplace that connects farmers in India to agricultural inputs, advisory services, and markets using a mobile-based technology platform. It outlines DeHaat's solution to improve access to quality agricultural services for 70 million small farmers in India. Key points include:
- DeHaat operates a mobile marketplace that provides farmers with inputs, advisory, and market linkages to increase their income up to 50% by improving efficiency.
- It has grown its farmer network to over 20,000 farmers through local operators and aims to reach 1 million farmers by 2020.
- The company generates revenue through input sales, market linkages, and leveraging farmer data analytics with
The journal publishes original works with practical significance and academic value. Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management. IJMRR is an international forum for research that advances the theory and practice of management. IJMRR is an international forum for research that advances the theory and practice of management. Organizational Behaviour, Rural Marketing, Business & Ethics International, Business & Ethics International, Business & Ethics International. All papers submitted to IJMRR are subject to a double-blind peer review process. All papers submitted to IJMRR are subject to a double-blind peer review process.
Day 3 abu syed, bangladesh centre for advanced studies (bcas), bangladesh, ar...ICIMOD
National meteorology institutions can improve climate services for agriculture by providing higher resolution weather projections and predictions to enable more effective early warning systems. A case study on the use of climate information in potato farming in Bangladesh found that sub-hourly weather data and high-resolution models are needed for crop modeling and early warnings. Current weather forecasts are not accurate enough for this purpose. Involving private sector partners could help disseminate services, increase sales of related products, and provide an embedded advisory service to contract farmers. National needs include improved climate information systems, water management technologies, and institutional support for early warning systems.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
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.
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.
IRJET- Smart Agriculture Assistant and Crop Price PedictionIRJET Journal
This document describes a smart agriculture assistant and crop price prediction web application. The application helps farmers by recommending which crops to grow based on soil sample analysis. It also provides fertilizer recommendations and predicted crop yields. Farmers can access daily live crop prices to help set selling prices. The application additionally predicts crop prices for the next 12 months. This helps farmers anticipate prices at harvest time. The system uses machine learning techniques like decision trees to generate crop price predictions based on past data. Screenshots demonstrate the system providing soil testing, crop/fertilizer recommendations, price data, and online agricultural product shopping to farmers.
This document describes a proposed agricultural management app called Agro-Master. It aims to provide full traceability and integrated technologies to monitor crops from farm to table. The app would use drone imaging, IoT sensors, data analytics, and blockchain to track supply chains, monitor crop health, optimize farming practices, and predict yields and market prices. It is presented as a way to differentiate from existing providers by offering these multiple solutions in one app. The app is also described as creating jobs, reducing waste to improve food supply and farmer incomes, and using direct marketing strategies to connect farmers and customers.
The document provides details of a business plan proposal for an agritech startup called Jai Shri Ram. It outlines key problems in Indian agriculture like increasing productivity and reducing costs. The proposed solution is a digital agriculture platform that provides farmers real-time information, precision farming tools, and supply chain optimization. It will distinguish itself through a comprehensive and personalized platform. The revenue model involves subscription fees and data analytics services. The impact will be improved livelihoods through enhanced productivity and sustainability in Indian agriculture.
This document presents a major project report on crop recommendations for agriculture using productivity and season factors. The report proposes developing a machine learning-based system to provide crop recommendations to farmers based on climatic and environmental factors. The proposed system aims to address the disadvantages of the existing word-of-mouth recommendation system by leveraging historical agricultural data and predictive analytics. If developed, the system would analyze soil parameters, temperature, rainfall and other climatic data to predict suitable crops and cultivation periods tailored to a specific farmer's location. This would help farmers select optimal crops and maximize agricultural output.
Smart Agriculture And Farmer's Assistance System On Machine LearningIRJET Journal
This document proposes a smart agriculture system that uses machine learning algorithms like random forest to help farmers make better decisions. The system collects data on temperature, humidity, soil parameters and other crop growth factors and uses this to classify crops into groups based on which grow best together on a given plot of land. It then provides ongoing recommendations and auction services to farmers on crop sales. The goal is to help farmers maximize yields by providing customized crop plans and real-time assistance based on machine learning predictions of weather and soil conditions.
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
IRJET- Rice Yield Prediction using Data Mining TechniqueIRJET Journal
This document discusses using data mining techniques to predict rice yield based on soil parameters. It proposes using a K-Nearest Neighbors (KNN) algorithm to analyze past agricultural data like temperature, rainfall, humidity and soil properties to determine the rice yield. The KNN algorithm calculates Euclidean distances between input parameters and existing data to find the K closest matches and predict output based on similarity measures. The approach aims to help farmers increase yields and avoid losses by providing accurate rice yield predictions based on different parameters. It concludes that soil properties and weather significantly impact rice yields and the proposed data mining method can predict yields but may not be fully accurate if weather changes.
Brian Fitzsimmons on the Business Strategy and Content Flywheel of Barstool S...Neil Horowitz
On episode 272 of the Digital and Social Media Sports Podcast, Neil chatted with Brian Fitzsimmons, Director of Licensing and Business Development for Barstool Sports.
What follows is a collection of snippets from the podcast. To hear the full interview and more, check out the podcast on all podcast platforms and at www.dsmsports.net
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2. TEAM NEWTON Sudhendra | Gaurav| Saurav
Contents
Workflow Model 3
Overview of Crop Damage & Farmer’s income 3
Main causes of low income in Indian Agriculture 3
Factors to be considered 4
Data Collection Technique 5
Predictive Analysis Model 6
KPIs 6
How to involve Farmers with our model 7
How to educate farmers to use our model 7
Scalability Plan for Next 10 years 8
Operations Sequence 8
Possible Problems & Backup plans 9
Collaborators 9
Introduction of Kirhsi Garntha 10
Backend systems of Krishi Grantha 10
Total Cost prediction 11
Social impact of our model 11
References 12
Appendix:
Cost Structure 13
BOT questioning structure to farmers 14
3. Live and personalized data
Processed information
Historical Data Farmers Historical Data
Intermediaries
Government
Institutes &
Departments
Factors Historical Data
Workflow Model
737 768 835
939 970 1025 1083 1145
Crop Damage Growth
0
5000
10000
15000
20000
Farmer Income vs India's Average
Income
Farmer Income India's Average Income
Overview of Crop Damage & Farmer’s income
TEAM NEWTON Sudhendra | Gaurav| Saurav
The education level of Indian farmers is comparatively low than farmers of other countries
Low volume of governmental investment for agriculture sector compared to the industrial sector
The average size of farm holdings in India is very low, less than 2 hectares or 5 acres
The inadequacy of such non-farm services as provision of irrigation, seed, finance, marketing,
etc.
Indian farmers have been using old and inefficient methods and techniques of production
generation after generation
Inadequate Irrigation Facilities
Main causes of low income in Indian Agriculture
4. Factors to be considered
Soil Quality
• PH
• Moisture
• Minerals
• Soil Fertility
Water Quality
• Water Hardness
• Water Pollution
Level
• Air Velocity
• Air Temperature
Air Quality
• Air Pollution
• Air Moisture
• Air Velocity
• Air Temperature
Pest attack
• Historical pest attack data
• Preferable pesticides related data for pest
control in every region
Oil price
• Oil price fluctuation
in India & Imports
country
• Data of
consumption
various crops in
accordance to oil
price
Market Information
• Demand in
market
• Accurate price in
various markets
• Last year reserves
• Consumer taste
change in market
Imports & Exports
• Data related to export countries demand &
supply
• Export Duties of various crops
• Government policies’ change in exports
Personalize farmers
data
• Farmer’s experience
• What kind of crops
farmer sow
• If any electrical
machine is used by
farmer
• Crops which is
familiar to farmer
Farm specific Data
• Quality of seeds
• Quality of pesticides
the farmer uses
• What are the others
crops are sown
• Timing of the
sowing
All the factors are sync. Connected with each
others
Factors would be considered historical as well
as present way
TEAM NEWTON Sudhendra | Gaurav| Saurav
5. Market Data like demand
data, price data of crops in
various regions for the last
10 years for getting the
trends
Use drones with air sensor to
get the air quality,
population level, air velocity,
air moisture etc.
Low cost Helium Balloon +
optical sensor for + VRT
Method to collect soil
related data with optical
analyzing
Data related with weather
change for the last 25 years
to predict the upcoming
change
Data of exports of crops to
different countries and price
of those crops for the last 10
years
Crop production data for last
10 years to get the
production pattern of the
crops
Data Collection Technique
Both historical & live data will require for accurate predictive analysis
Live Data Historical Data
• Farmers Personalized data
• Market demand & price
• Oil price movement data
• Any change in export duty, policy
or foreign demand
• Weather change data
• Soil quality change
• Air quality and pollution level change
• Pest attack in various regions in different times
• Water quality & pollution level data
*All data will be stored in Microsoft cloud in a specified format of database to maintain data integrity
All historical data will be updated after every 6 months and live data updated regularly
Pest attack in various regions
in different timings of year
for last 10 years
Water quality level in
different regions and
pollution level for last 10
years
AI bot will collect
personalized data
from farmers with
feature of Cortana
based language
Cloud Sync.
TEAM NEWTON Sudhendra | Gaurav| Saurav
6. Predictive Analysis Model
Sensor Interface
MQTT Broker
FTP Server
Deep
learning
Predictive
Analysis with
Neural
Network
Mapping
of
Future
Data
soil quality
air quality
pest attacks
Crop yield
water quality
Market price &
demand
imports of crops
Database with
historical data &
farming information
Define best possible solution for
farmers to increase income
Cloud Sync.
KPIs
14% 25%
28%33%
KPI A
KPI C KPI D
KPI B
Rough estimation of weights of KPIs
TEAM NEWTON Sudhendra | Gaurav| Saurav
Predictive Index
Compare
predictive
data with
historical
data
Inform farmers through
mobile message
Difference between actual and
predictive crop production
KPI B
KPI C KPI D
How average income and
production increases
KPI A
Decreased in crop damage in
our operating areas
Farmers involvement
7. Farmer
involvement
Cycle
Collectio
n farmer
historica
l data
Farmers
provide
crop
informat
ion
Farmers
use the
system
Feedbac
k
System
Upgrade
Collaborate with Rural Innovators
Recognizing and collaborating with rural
innovators who are working with farmers will
help in increasing the awareness of the
program
Involve Local NGO’s and Influencers
Involving local NGOs will help in increasing the
reach of the program and will also lead to
greater acceptance as these influencers
command greater trust
Gram Panchayats and Government Agencies
As panchayats are the governing body in the
villages, collaborating with them will lead to
greater farmer involvement
Partner with Krishidarshan
Create informative and promotional program
on Krishidarshan(TV channel funded by
government)
1
2
3
4
How to involve Farmers with our model
How to educate farmers to use our model
5
1
4
3
2
Providing content videos of farmers
who got success with our model
Demo of our model through videos in
local languagesFree training of how to use our model
Use radio & TV channels to increase
more to the farmers
Always help the farmers in their
queries
TEAM NEWTON Sudhendra | Gaurav| Saurav
8. Scalability Plan for Next 10 years
Operations Sequence
2019 2020 2021 20232022 2024 20262025 2027 2028
Phase 1 Phase 2 Phase 3
Farmers
Involve
ment
Increase
operatio
nal
regions
Accuracy
Rate
Future
Plan
A
B
C
D
Focus on try to involve more farmers to
use our model to increase their income
• Start with the region where farmer is most educated
• After 1-1.5 years will expand to the most damage prone area for crops
• Next 2-4 years will expand to the areas where farmers will be willing to
involve mostly with our model
2029
• Introduce Krishi Grantha, a tablet like device for
farmers in the most involved region of farmers
• Start with 1000 devices
• Initial target accuracy rate is 95%-97% for predicting all factors
• From 2022 we will try to improve this rate to 99.5% by gathering
more data with accurate database maintaining
TEAM NEWTON Sudhendra | Gaurav| Saurav
Involving farmers
will improve
accuracy rate
Operation should
increase when we will
get enough success in
current regions
With time our operation accuracy
rate should increase, if not then
are errors in our model
Krishi Grantha will be
introduced based on farmers
involvement & accuracy rate
9. Possible Problems & Backup plans
Problems Possible Solutions Implementation
Regular
Review
Long term & Short term thinking
Long term
Short term
* Krishi Grantha will be introduced based on the farmers involvement to our model
Collaborators
Associate with Government
organization
• BSNL (Network Related problem)
• Census of India (Historical data)
Associate with private organization
• Nebulaa (Automatc grain
analyzer)
• PlantMD (3D image processing)
• Yesbank
TEAM NEWTON Sudhendra | Gaurav| Saurav
10. Connected through TCP server
with Microsoft Cloud to get the
historical data and predictive
analysis of data
Introduction of Kirhsi Garntha
High Resolution camera with
image processing technology
to to get the RGB value of
corps
Connection with Automatic Grain
Analyser of Nebulaa to compare
expected quality with actual one
Will suggest farmers how to improve
crop quality by accurate fertilize &
pesticides. Also suggest actual price of
the crops in market
According to our plan in 2025-2027 we are going to introduce Krishi Grantha, a tablet like AI driven device for
farmers such that they can measure their crop production & income level individually
Backend systems of Krishi Grantha
Nebulaa Printer Microsoft Cloud Sync. 3D image processing sensor
Just need to put your seed
and it will analyze your
seed quality
Cloud sync. to get all the
historical and live data of market
and crop production
High resolution camera with 3D
image processing sensors to get
RGB value of crop to analyze crop
health
Demo for Nebulaa printer:
https://www.youtube.com/watch?time_continue=69&v=UU4PXeahFhc
Demo For 3D image processing technique:
https://www.youtube.com/watch?v=UrxIIbkpkbo
Cloud Sync.
TEAM NEWTON Sudhendra | Gaurav| Saurav
11. Total Cost prediction
Cost Drivers
Social impact of our model
• Drones
• Sensors
• Helium Balloon
• Server
• Tablet
• Server
• Bot
• Data
• Sensors
• VRT Technology
• AI Development
• Developer
• Server Maintainer
• AI Maintainer& DeveloperHuman Resource Cost
Develop
ment
cost
Maintenance
Cost
Equipment Cost
Total
Cost
Estimated cost for 10 years : Rs.509,52,14,000
Lifestyle of farmers
will improve
Farmers income will
increase
Crop Damage will
decrease &
production rate will
increase
Loan taken by farmers
will decrease
Net
producti
on &
exports
will
increase
for
country
Suicide rate of
farmers will decrease
TEAM NEWTON Sudhendra | Gaurav| Saurav
13. From which region
you are from?
1.
North
2.
West
3.
South
4.
East
For How Many
years you
associated with
sowing?
1.
<5 years
2.
5-15 years
3.
15-25 years
4.
>25 years
What kind of crops
you use to sow?
1.
Option 1
2.
Option2
3.
Option 3
4.
Option 4
Do you have
machinery for
cropping
1.
Yes
2.
NO
Is water supply is
prominent in your
field?
1.
Yes
1.
No
In which month
you sow your
seeds?
1.
Jan
2.
Feb
3.
Mar
ch
4.
Apri
l
5.
Ma
y
6.
Jun
e
7.
July
8.
Aug
ust
9.
Sep
tem
ber
*
Oct
obe
r
0.
Nov
em
ber
#.
Dec
em
ber
When do you cut
your crops?
1.
Jan
2.
Feb
3.
Mar
ch
4.
Apri
l
5.
Ma
y
6.
Jun
e
7.
July
8.
Aug
ust
9.
Sep
tem
ber
*
Oct
obe
r
0.
Nov
em
ber
#.
Dec
em
ber
Do you have any
machinery for
sowing?
1
Yes
2.
No
If Yes, then what
kind of machinery
do you have?
1.
Option 1
2.
Option 2
3.
Option 3
4.
Option 4
What kind of
pesticides do you
use?
1.
Option 1
2.
Option 2
3.
Option 3
4.
Option 4
What kind of
fertilizer do you
use>
1.
Option 1
2.
Option 2
3.
Option 3
4.
Option 4
Appendix 1. BOT questioning structure to farmers
(All the numbers & symbols will be represented by mobile keyword.
All questions will be asked in farmers local language)
TEAM NEWTON Sudhendra | Gaurav| Saurav