Abstract: Most agricultural crops have been badly affected by the effect of global climate change in India. In terms of their output over the past 20 years. It will allow policy makers and farmers to take effective marketing and storage steps to predict crop yields earlier in their harvest. This project will allow farmers to capture the yield of their crops before cultivation in the field of agriculture and thus help them make the necessary decisions. Implementation of such a method with a web-based graphic software that is simple to use and the machine learning algorithm can then be distributed. The results obtained are granted access to the farmer. And yet there are various methods or protocols for such very data analytics in crop yield prediction, and we are able to predict agricultural productivity with guidance of all those algorithms. It utilizes a Random Forest Algorithm. By researching such problems and issues such as weather, temperature, humidity, rainfall, humidity, there are no adequate solutions and inventions to resolve the situation we face. In countries like India, even in the agricultural sector, as there are many types of increasing economic growth. In addition, the processing is useful for forecasting the production of crop yields.

Keywords: predictive modeling, feature selection, data preprocessing, regression analysis, agricultural forecasting, remote sensing data, weather data, soil data, precision agriculture, crop management, decision support system, machine learning models, data – driven farming, yield optimization, agriculture technology, big data in agriculture.

Works Cited:

Shanmuga Priya M, Lin Eby Chandra M, Kumaran M "CROP YIELD PREDICTION USING RANDOM FOREST ALGORITHM ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 105-108, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12918

PDF | DOI: 10.17148/IJARCCE.2023.12918

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