Crop yield prediction is an important task in modern agriculture that helps farmers and agricultural organizations make better decisions regarding crop management, irrigation, fertilizer usage, and harvesting. Traditional prediction methods often fail to provide accurate results due to changing climatic conditions and complex environmental factors. Recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) have enabled the development of intelligent systems capable of analyzing large amounts of agricultural and weather-related data to predict crop yield accurately. This paper presents a comprehensive study of machine learningbased crop yield prediction using weather data such as temperature, rainfall, humidity, soil moisture, and atmospheric conditions. Various machine learning algorithms including Linear Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, and Neural Networks are analyzed for their effectiveness in crop yield prediction. The study also identifies limitations in existing systems such as lack of real-time weather integration, limited dataset diversity, and poor adaptability to changing climatic conditions. A hybrid machine learning framework is proposed that combines historical crop data, weather parameters, and soil conditions to improve prediction accuracy. The proposed system aims to assist farmers in increasing productivity, reducing risks, and promoting sustainable agriculture practices.
P et al. (Tue,) studied this question.