Remote Sensing Based Crop Health Classification Using NDVI and Fully Connected Neural Networks

Accurate crop health monitoring is not only essential for improving agricultural efficiency but also for ensuring sustainable food production in the face of environmental challenges. Traditional approaches often rely on visual inspection or simple NDVI measurements, which, though useful, fall short in detecting nuanced variations in crop stress and disease conditions. In this research, we propose a more sophisticated method that leverages NDVI data combined with a Fully Connected Neural Network (FCNN) to classify crop health with greater precision. The FCNN, trained using satellite imagery from various agricultural regions, is capable of identifying subtle distinctions between healthy crops, rust-affected plants, and other stressed conditions. Our approach not only achieved a remarkable classification accuracy of 97.80% but it also significantly outperformed conventional models in terms of precision, recall, and F1-scores. The ability to map the relationship between NDVI values and crop health using deep learning presents new opportunities for real-time, large-scale monitoring of agricultural fields, reducing manual efforts, and offering a scalable solution to address global food security.

Advancements in Ship Detection: Comparative Analysis of Optical and Hyperspectral Sensors

In marine surveillance, applications span military and civilian domains, including ship detection, marine traffic control, and disaster management. Optical and hyperspectral satellites are key for this purpose. This paper focuses on ship detection and classification techniques, particularly comparing optical and hyperspectral remote sensing approaches. It presents a comprehensive analysis of these technologies, covering feature extraction, methodologies, and their suitability for different missions. The study highlights the importance of selecting the right sensor aligned with mission objectives and conditions, aiming to improve detection accuracy through integrated strategies. The paper examines the strengths and limitations of both technologies in various maritime applications, enhancing understanding of their usability in different operational scenarios.