DRIVEN FRAMEWORK FOR EARLY DETECTION OF PLANT STRESS USING MULTI-SOURCE REMOTE SENSING DATA AND MACHINE LEARNING TECHNIQUES AI-

In this article, we suggest an approach of machine Learning technique to investigate a set of multisource remotely sensed data to diagnose plant stress early. The model of this work is the combination of satellite images, high-resolution data using UAVs and a hyperspectral sensor aimed at capturing stress factors, including drought, pests, and nutrient deficiencies, to maximize the health of plants. The system is developed based on state-of-the-art machine learning models, support vector machines (SVM), convolutional neural networks (CNN) and long short-term memory (LSTM) networks are used to accurately analyze and forecast plant stress by utilizing the data. Its efficacy on various datasets is confirmed by experiments that demonstrate impressive results compared to the state-of-the-art approach. The hybrid model is 94.5% higher than the other models, SVM, CNN, and LSTM. In precision agriculture, early warning of the model can be used to efficiently utilize resources, reduce crop losses, and improve yield quality. The given method offers an implementable and scalable solution to the problem that could be applied to commercial-level agricultural systems and stresses the importance of the given method in facilitating the sustainability and agricultural decision-making methods of modern farm systems.