AI-Based Crop Yield Prediction Using Remote Sensing and Deep Learning Techniques

Abstract

The increasing demand for accurate and scalable crop yield estimation under changing environmental conditions has driven the adoption of advanced technologies in modern agriculture. This study presents an artificial intelligence-based crop yield prediction framework that integrates remote sensing data with deep learning techniques to enhance forecasting accuracy and decision-making. The proposed methodology utilizes multi-temporal satellite imagery, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), along with environmental variables such as temperature, rainfall, soil properties, and crop growth parameters. These datasets are preprocessed through image correction, normalization, and feature extraction techniques to ensure consistency and reliability. Deep learning architectures, particularly Convolutional Neural Networks and Long Short-Term Memory networks, are employed to capture spatial and temporal patterns influencing crop development and yield variability. The system is designed with real-time data integration and adaptive learning capabilities, enabling continuous monitoring and dynamic prediction across large agricultural areas. The models are trained and validated using region-specific datasets to ensure robustness across diverse agro-climatic conditions and crop types. Experimental results demonstrate that the proposed approach significantly improves prediction accuracy and reduces forecasting errors compared to traditional statistical and machine learning methods. Additionally, the integration of remote sensing enables non-invasive, large-scale monitoring, supporting timely and informed decision-making for irrigation, fertilization, and crop management practices. The findings highlight the effectiveness of combining artificial intelligence with deep learning and satellite-based observations for precision agriculture applications. The study concludes that AI-based crop yield prediction systems offer a scalable, efficient, and data-driven solution for enhancing agricultural productivity, optimizing resource utilization, and promoting sustainable farming practices.