Weather Forecast-Based Crop Recommendation Using Machine Learning

Abstract

Agriculture is highly dependent on weather conditions, which directly influence crop productivity and farmers’ decision-making. Traditional crop selection methods rely on historical data and experience, which are often inadequate due to unpredictable climatic changes. This paper presents a Weather Forecast-Based Crop Recommendation System that uses machine learning techniques to suggest suitable crops based on real-time weather parameters such as temperature, humidity, and rainfall. The proposed system employs Random Forest and Convolutional Neural Network (CNN) models to analyse environmental conditions and generate accurate crop recommendations. The application is developed using Python for backend processing and HTML, CSS, and JavaScript for the frontend interface. Experimental results show that the system improves crop selection accuracy and supports sustainable farming practices by enabling data-driven agricultural decisions.