Crop Yield Prediction Using AI: A Review

Forecasting agricultural productivity is a crucial undertaking in agriculture, as it aids in making informed decisions on crop cultivation and strategizing. There has been a growing fascination in utilizing artificial intelligence (AI) methods to forecast crop yields in recent years. This study offers a comprehensive analysis of the present status of AI-based crop yield prediction research. It covers the methodologies and data employed, as well as the problems and constraints associated with these approaches. The review emphasizes the utilization of machine learning methods, including decision trees, neural networks, and support vector machines, for the purpose of predicting crop productivity. These algorithms have been utilized on diverse agricultural data sets, encompassing climate, soil, and remote sensing data, along with crop management approaches. Researchers have investigated the incorporation of various data sources and the utilization of ensemble models to enhance the precision of agricultural production forecasting. A significant obstacle in utilizing AI for crop production prediction is the limited accessibility and reliability of data, together with the diverse and inconsistent nature of data across various agricultural regions. Transparent and interpretable models are necessary to establish trust and comprehension in the predictions generated by AI systems. Another obstacle is in the scalability and applicability of AI models to various crops and management approaches.