Design and Execution of High Reliability PDDC-Net Model for Classifying and Identifying Plant Leaf Disease Using Deep Learning

The imaginative capacity of a nation is contingent upon the development and performance of its agriculture sector. Agricultural, as the primary source of raw materials and food, serves as the fundamental basis for all nations. Agriculture serves as a significant provider of sustenance for human populations. The identification of diseases of plants has become increasingly crucial as a result. The following are established methodologies for the identification of plant diseases. Nevertheless, the identification of leaf diseases frequently involves the utilization of visual inspection by plant pathologists or agricultural professionals. The process of identifying plant leaf disease using this approach can be subjective, costly, and time-intensive. It typically necessitates the involvement of a large team of experts possessing extensive knowledge of plant infections. The identification of plant leaf diseases can also be facilitated through the utilization of a software solution that has undergone rigorous experimental evaluation. In contemporary times, the utilization of deep learning and machine learning has become prevalent. The primary objective of this endeavor is to implement the utilization of deep learning models in the deployment of Plant Disease Identification and Classification (PDDC-Net). The procedure of preparation additionally encompasses the elimination of diverse sources of noise, thereby restoring the pictures within the information set. Moreover, the PDDC-Net employs Convolutional Neural Networks (CNNs) based on residual networks (ResNet-CNN) for the purpose of extraction of characteristics and classification. Based on empirical evidence, the proposed PDDC-Net model demonstrates a commendable level of reliability in the identification and classification of illnesses of the leaves.