Crop Disease Prediction & Solution Recommendation System Using Chatbots

Agriculture plays a crucial role in economic development, yet farmers often face challenges in selecting suitable crops, managing soil fertility, and preventing diseases, which ultimately affect productivity and profitability. This paper presents an intelligent Crop Prediction and Solution Recommendation System that integrates Machine Learning (ML) techniques to assist farmers in making data-driven decisions. The system focuses on analyzing soil properties, predicting appropriate crops, and recommending fertilizers and preventive measures for diseases and pests. The proposed system collects real-time soil data such as moisture, temperature, and nutrient content using embedded sensors and microcontrollers. This data is combined with historical datasets obtained from agricultural sources and undergoes preprocessing steps including cleaning, normalization, and feature scaling to ensure accuracy. Machine learning algorithms such as Random Forest and Linear Regression are applied to predict crop suitability and expected yield. Additionally, pattern recognition techniques are used to identify potential disease occurrences and provide preventive recommendations.