A Comprehensive Survey on Phytopathogen Surveillance with Modern Artificial Intelligence Practices

The most important aspect of modern agriculture is the detection of plant diseases with the goal of improving crop quality and output. This survey paper investigates modern approaches of deep learning algorithms, Explainable AI and Federated Learning to plant disease diagnosis. The background and broadening of plant disease detection are briefly reviewed at the outset of the study, with a focus on the importance of precise and effective identification techniques. The multivariate normal DL neural network (MNDLNN) classifier and Customized CNN are the most robust feature extraction and classification techniques among the examined technologies. Using Higher-Order Whitened Singular Value Decomposition (HOWSVD), complex data is processed in a way that highlights distinct patterns and features for easy data classification and also increases accuracy. The E-GreenNet generates disease classification features based on a MobileNetV3. The ResNet9 and Improved Vision Transformer models perform better when processing complicated visual data. Spectral Generative Adversarial Neural Network (DSGAN2) and Lite Multikernel Depthwise Convolutions architecture are suitable for real-time applications because they provide promising results with less computational overhead. These findings' implications point to a possible move toward disease detection systems that are easier to use and more effective, which would improve crop management and food security. The development of more affordable solutions for small scale farmers, the integration of Embedded systems for real-time monitoring, and the improvement of these models for increased precision are some future directions. This survey highlights the transformative potential of combining deep learning with other techniques like Federated Learning in advancing agricultural practices.