AI-Based Crop Recommendation System for Farmers Using Machine Learning and Full-Stack Web Technologies
Keywords:
Agricultural AI, Android, Crop recommendation, Flask, Machine learning, MySQL, Precision agriculture, Random forest, Smart farming, Soil analysisAbstract
This paper presents the design, development, and experimental evaluation of an AI-based crop recommendation system that enables farmers and agricultural users to receive accurate crop predictions based on soil nutrient composition and prevailing weather conditions. The system employs a Random Forest Classifier trained on a dataset of 2,200 records encompassing seven agronomic parameters: nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall and achieves a test accuracy of 97.5%, a weighted F1-score of 0.977, and a 10-fold cross-validation accuracy of 97.2% (σ = 0.4%). The system architecture integrates a Python Flask web application, a MySQL relational database, a ReportLab-based PDF report generator, Matplotlib-based data visualization, and a multilingual Android application developed in Java. Recommendations are served through a REST API with a mean response time of 112 ms, with all 44 API test cases and 50 functional web tests passing after validation. The Android application successfully verified all 22 crop categories across English, Hindi, and Marathi language interfaces. Real-world validation in two Maharashtra districts produced crop recommendations consistent with regional agricultural practice. The system addresses the critical gap in affordable, intelligent, and accessible agricultural decision-support tools for rural Indian farmers.
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