Abstract: In this paper, a machine learning-based system that helps South Karnataka farmers choose appropriate medicinal crops is presented. It combines a web-based platform that offers real-time recommendations and historical trend storage with a trained Random Forest model. 12,800 samples from 8 classes of medicinal crops and 17 input features, such as soil nutrients, micronutrients, climate, and geographic indicators, are included in the dataset. On a stratified split, the Random Forest classifier’s test accuracy was 58.59%. Water availability, temperature, and pH are important influencing factors. The model was implemented as part of a comprehensive smart farming solution that included a MongoDB database, React frontend, and Node.js backend.
Keywords: Crop Recommendation, Machine Learning, Smart Farming, Medicinal Plants, Random Forest
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DOI:
10.17148/IJARCCE.2025.141258
[1] Abhilash L Bhat, Sahana C S, Supreeth V, Thanuja T, Tilak Gowda M Y, "Med-Crop Recommendation: A Smart Farming Platform for Medicinal Crop Selection using Machine Learning," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141258
