Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

Forecasting crop yield prediction is an very important decision support tool. Crop yield prediction is the process of using data and technology to estimate how much agricultural produce, such as crops, will be harvested in a specific area for a given period. This involves analyzing various factors like weather patterns, soil quality, historical data, and farming practices to make educated guesses about the future harvest. It helps farmers, policymakers, and food supply chain stakeholders make informed decisions regarding planting, resource allocation, and food security. Essentially, it's like forecasting the future of crop production to ensure efficient and sustainable agriculture. Forecasting crop yield is a complex and vital endeavor in modern agriculture, aiming to foresee the quantity of crops that will be harvested in a specific area during a particular growing season. This process relies on a synergy of data-driven analysis, technological advancements, and the integration of various factors that influence crop growth and productivity. In essence, it involves the art and science of anticipating nature's bounty, allowing us to make informed decisions, enhance food security, and optimize resource allocation.

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Soil Type Grading and Crop Prediction

Sonawane¹,

Sankpal²,

Bandgar³

et al. 2022

IJARSCT

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In general, agriculture is the backbone of India and also plays an important role in Indian economy by providing a certain percentage of domestic product to ensure the food security. But now-a-days, food production and prediction is getting depleted due to unnatural climatic changes, which will adversely affect the economy of farmers by getting a poor yield and also help the farmers to remain less familiar in forecasting the future crops. This research work helps the beginner farmer in such a way to guide them for sowing the reasonable crops by deploying machine learning, one of the advanced technologies in crop prediction. Naive Bayes, a supervised learning algorithm puts forth in the way to achieve it. The seed data of the crops are collected here, with the appropriate parameters like temperature, humidity and moisture content, which helps the crops to achieve a successful growth. In addition as the software, a mobile application for Android is being developed. The users are encouraged to enter parameters like temperature and their location will be taken automatically in this application in order to start the prediction process.

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Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

Cited by 3 publications

References 1 publication

Crop Prediction from Soil Parameters using Light Ensemble Learning Model

Crop Prediction from Soil Parameters using Light Ensemble Learning Model

Forecasting Crop Yield For Sustainable Agriculture

Soil Type Grading and Crop Prediction

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