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  Scopus ID: 21100926589

Harvest Prediction by KNN Classification

J. Yeswanth Sai Mahesh, A. Mourya Bharadwaj, Shravan Nikhil and V. Balaji


Agriculture is an important field all over the world where there are many challenges facing by the farmers. This has become a problem for developing countries. Using latest technologies many companies are using Internet of Things based services to reduce manual work. These methods are mostly useful in the case on reducing human work but not in prediction process. In this project crop yield prediction using latest machine learning technology and KNN (K-Nearest Neighbor) classification algorithm is used for prediction crop yield based on soil, temperature, and weather factors. We also prepared dataset with various soil conditions as features and labels for predicting type of each label is related to certain crop. In prediction process farmers can give input their soil features and result will be type of crop suitable for specific conditions and application also helps in suggesting best crops.

Published on: December 04, 2023
doi: 10.17756/nwj.2023-s4-074
Citation: Mahesh JYS, Bharadwaj AM, Nikhil S, Balaji V. 2023. Harvest Prediction by KNN Classification. NanoWorld J 9(S4): S440-S442.

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