Intelligent Crop Stress Detection Using Artificial Intelligence and Multispectral Imaging

Abstract

The increasing impact of environmental stressors such as drought, nutrient deficiency, and pest infestation on crop productivity has necessitated the development of advanced detection systems for timely intervention. This study presents an intelligent crop stress detection framework that integrates artificial intelligence with multispectral imaging to enable accurate and early identification of stress conditions in crops. The proposed methodology utilizes multispectral data acquired from satellite and drone-based sensors, capturing spectral information across visible, near-infrared, and red-edge bands, along with environmental parameters and ground-truth crop health data. These datasets are preprocessed through image correction, noise reduction, normalization, and feature extraction techniques to ensure consistency and reliability. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) are derived to assess plant health and detect anomalies. Advanced machine learning and deep learning models, including Convolutional Neural Networks, Random Forest, and Support Vector Machines, are employed to capture complex spatial and spectral patterns associated with various stress factors. The system is trained and validated using diverse datasets to ensure robustness across different crop types and agro-climatic conditions. Experimental results demonstrate that the proposed approach achieves high detection accuracy and significantly outperforms conventional field-based and low-resolution monitoring methods. The integration of multispectral imaging enables large-scale, non-invasive, and real-time monitoring, supporting timely and targeted agricultural interventions such as precision irrigation and nutrient management. The findings highlight the effectiveness of combining artificial intelligence with spectral imaging for precision agriculture applications. The study concludes that intelligent crop stress detection systems provide a scalable, efficient, and data-driven solution for improving crop health monitoring, enhancing productivity, and promoting sustainable agricultural practices.