Enhancing Crop Yield through Efficient Anomaly Detection Using Transfer Learning and Multispectral Satellite Imagery

The increasing demand for sustainable agriculture necessitates innovative approaches for monitoring and enhancing crop health. Data-driven methods, combined with advanced machine learning models and remote sensing technologies, present significant potential to bridge the gap between early anomaly detection and timely intervention. Our research explores the development of a robust system integrating state-of-the-art deep learning techniques with transfer learning and multispectral satellite imagery to detect crop anomalies. The proposed system leverages publicly available datasets to identify early symptoms of crop stress—such as yellowing, spotting, and wilting—in key crops, including corn, soybeans, wheat, and sunflowers. Furthermore, the study investigates the influence of environmental factors, such as lighting conditions, weather patterns, and soil characteristics, on detection accuracy. By providing actionable insights for optimizing intervention strategies, this research aims to advance sustainable agricultural practices and improve crop yields. The work also contributes to the broader field of data science by demonstrating the application of sophisticated models in tackling complex agricultural challenges.