Leveraging Super-Resolution Technology in Drone Imagery for Advanced Plant Disease Diagnosis and Prognosis

The economy of developing nations is heavily reliant on agriculture. For agriculture to remain sustainable and productive, a number of important challenges must be addressed. Plant diseases are among the most alarming issues. To address the challenge of identifying plant diseases from drone-captured images, which often lack sufficient resolution and may lead to mis-classification, we propose Vision-based Image Generation and Analysis Model (VIGAM) framework as a solution. In this study, we introduce the VIGAM framework, a deep learning model-based solution that shows promise as a useful instrument in a variety of plant protection strategy. This comprehensive method using computer vision-based models aids in the interpretation of plant images taken by drones, which are then used to detect plant diseases and offer accurate diagnosis. Large plant picture databases can be analyzed by ML systems to find early indicators of infections or illnesses. Farmers can quickly respond with tailored treatments or the removal of afflicted plants thanks to image recognition models that can identify patterns and symptoms associated with particular plant diseases. Our method is feasible for real-world agricultural applications, and through experiments, we demonstrate that the proposed framework effectively enhances plant identification and disease detection by incorporating high-resolution drone images.