Advanced Framework for Automated Plant Disease Diagnosis: Integrating Convolutional Neural Networks with Transfer Learning Strategies for Enhanced Classification Accuracy and Robustness

Abstract

In order to reduce the speedy spread of plant diseases and protect global food security, sophisticated early detection and diagnostic technologies are necessary. This research proposes a novel framework that integrates transfer learning with state-of-the-art machine learning models to autonomously identify plant diseases. The major objective is to establish a trustworthy system that can diagnose plant illnesses from images so that farmers and other agricultural specialists may take necessary action. Our technique combines CNNs with transfer learning algorithms utilizing VGG16, VGG19, ResNet50, and InceptionV3. To execute in-depth tests, a broad variety of plant diseases from varied climatic situations and crop sorts were applied. The generality and performance of the model were increased by picture scaling, normalization, and data replenishment. Our data suggest that transfer learning enhances resistance and classification accuracy for a variety of plant disease categories. Our tests showcase the specific aspects of every model design, showing its multiple responsibilities and performance indicators. Through comparison and assessment, we construct model configurations that are ideal for activities requiring the diagnosis of sickness. Our work provides an automated technique for plant disease diagnosis that is both scalable and efficient, with repercussions that transcend beyond the agricultural sector. In addition to increasing disease detection and management procedures, the recommended methodology increases yield optimization and sustainable agricultural production. Future research targets include multi-modal data integration, real-time monitoring systems for projected sickness reduction, and enhanced deep learning. By overcoming issues with plant health monitoring and ensuring global food security in an age of altering agricultural landscapes and environmental pressures, this initiative supports precision agriculture and agricultural technology.