CROP RECOMMENDATION SYSTEM USING ML ALGORITHMS

Abstract

Abstract - Agriculture is the backbone of India's economy, crucial for the well-being of its people. Ensuring the production of high-quality crops is essential for maintaining a healthy lifestyle. Analyzing environmental and soil conditions, including factors such as moisture and pH levels, temperature, and chemical composition, is vital for cultivating superior crops. Predicting crop yields has become increasingly challenging due to unpredictable weather patterns caused by global warming, resulting in crop destruction, food scarcity, and tragic consequences such as farmer suicides. This study aims to develop a website utilizing machine learning models for crop recommendations, taking into account inputs such as pH values, temperature, and soil parameters. Various machine learning algorithms, including SVM, logistic regression, naive bayes, and Random Forest, are utilized, with Random Forest demonstrating superior prediction capabilities. These systems carefully analyse diverse factors, including soil quality, climate data, and past crop performance, to suggest optimal crops tailored to specific locations. Accessible through user-friendly platforms, crop recommendation systems empower farmers to harness the benefits of technology and data, thereby enhancing agricultural productivity, financial gains, and global food security.

Ultimately, these systems serve as a crucial tool in advancing modern agriculture, leveraging technology and data analysis to assist farmers in making decisions that contribute to food security and agricultural sustainability.

Key Words: Random Forest, machine learning model, moisture and pH level, temperature, and chemical composition, recommendation system, factors.