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Analysis and Classification of Crop Recommendation System Using ML Technique

Author(s) K Bhanu Prakash, S. Shiva Shankar
Country India
Abstract In rural India, agriculture is the principle supply of earnings for lots and performs a critical role inside the United States of America’s economic system. But compared to different land, the crop yields in keeping with hectare are low. Low yields make it difficult for small farmers to make a residing, leading to financial worry and even loss of life. The look at proposes a time-based method that makes use of pesticide and weather information to assist farmers predict crop yields. The system makes use of GPS to tune farmers and is to be had on a mobile app. Farmers enter facts about climate, pesticide use, soil type and land length. The device then uses machine studying to advocate the satisfactory vegetation to plant or predict the yield of a particular crop. The machine gaining knowledge of strategies used by this system consist of Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Jump (RF), Multivariate Linear Regression (MLR), and K-Nearest Neighbor (KNN). With 95% accuracy, the Random Forest approach gave the first-rate consequences out of those. Other algorithms additionally confirmed excessive overall performance with ANN at ninety two%, SVM at ninety%, KNN at 88%, and MLR at 85%. This algorithm not handiest predicts crop yield, however additionally advises farmers on when to use insecticides and fertilizers to improve crop boom. This technology uses climate and pesticide information to assist farmers make higher choices, produce more crops, and earn extra cash.
Keywords Rural India, Farming, Low Yield, GPS weather, Pesticide Use, Soil Type, Land Size, Crop Recommendations, Random Forest, Better Decisions
Field Engineering
Published In Volume 6, Issue 4, April 2025
Published On 2025-04-14
Cite This Analysis and Classification of Crop Recommendation System Using ML Technique - K Bhanu Prakash, S. Shiva Shankar - IJLRP Volume 6, Issue 4, April 2025.
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