International Journal of Science, Engineering and TechnologyAuthors: Dr. Pritesh Patil, Vinay Basargekar, Shraddha Thorbole, Saurabh Rai
Abstract: Satellite imagery has become an essential component of modern precision agriculture, enabling large-scale observation of crop health conditions. However, many existing monitoring solutions primarily provide vegetation indices, stress maps, or threshold-based alerts that can be difficult for farmers to interpret and act upon. To address this limitation, this study presents a cloud-native decision-support framework that integrates multispectral satellite observations, weather information, machine learning, and explainable recommendations. The proposed framework uses key vegetation indicators, including NDVI, NDWI, and SAVI, from Sentinel-2 satellite imagery and combines them with environmental parameters such as rainfall, temperature, and humidity. A Random Forest classifier is utilized to categorize crop conditions into four stress levels: healthy, mild, moderate, and severe. To enhance transparency and usability, an explainability module identifies the primary factors influencing each prediction and translates them into actionable recommendations, such as checking irrigation systems, assessing nutrient deficiencies, or maintaining routine field monitoring. The system follows a modular cloud-native architecture including data retrieval, preprocessing, feature engineering, model prediction, and dashboard visualization. The key contribution of the proposed framework is its ability to convert analytical results into actionable insights. Beyond detecting crop stress levels, it provides explanations for the underlying causes and recommends suitable management practices, enabling farmers to make informed decisions.
DOI: http://doi.org/
