PLANT DISEASE DETECTION BY USING MOBILENTV2 AND XCEPTION ON FILTERED AND ENHANCED IMAGES

Abstract

The gathering, sorting, and processing of plant leaf images serves as the foundation for this study. These are crucial first steps in the plant health monitoring process that guarantee reliable findings. The work classifies and detects plant leaf photos, extracting data on plant health using state-of-the-art deep learning models like Xception and MobileNetV2. In order to assess the effectiveness of the system, additional filters are applied to the photos of plant leaves in order to adjust characteristics like brightness, contrast, sharpness, and blur. The study's results show that the deep learning models employed could accurately determine the health of plant leaves, offering important new information for related future research.