Agriculture plays a vital role in the economy of India, acting as the primary source of livelihood for nearly 58% of the country's population. However, the sector faces significant challenges, including unpredictable weather conditions, pest attacks, and improper fertilizer usage, which often lead to reduced crop yields and economic instability for farmers. This paper presents a comprehensive Crop Yield Prediction and Agricultural Advisory System utilizing advanced machine learning techniques. The proposed system predicts crop yield based on critical environmental factors such as rainfall, temperature, and pesticide usage. Additionally, it integrates specialized modules for disease risk assessment, crop recommendation, and fertilizer recommendation, creating a holistic decision-support platform. The models are trained on large-scale historical datasets. Specifically, the study employs Random Forest for yield and disease prediction, Multinomial Logistic Regression for crop recommendation, and a rule-based system for fertilizer dosage. The system is implemented using the Flask web framework, providing an interactive and user-friendly interface for stakeholders. The experimental results demonstrate that the system can assist farmers in making data-driven decisions, optimizing resource allocation, and significantly improving agricultural productivity and sustainability.
Gaikwad et al. (Thu,) studied this question.