Segmentation Approaches for Accurate Plant Leaf Disease Identification by Integrating Pattern Recognition and Hybrid Deep Learning Methodologies

Abstract

Diseases of plant leaves have a great impact on agricultural production; on the yield of crops, as well as the quality. Traditional plant disease identification is mostly done by manual observation, which takes more time and is not always consistent, because it depends on the experience and judgment of the person, so sometimes wrong identification can happen, leading to delay in treatment, extra cost, and finally affecting the crop yield and quality. This paper proposes automated plant leaf disease detection using advanced pattern recognition and deep learning. The method combines digital image processing with deep learning to derive discriminative leaf features. The suggested method combines the digital image processing and the deep learning model to derive discriminative features of leaf images. The first step involves using preprocessing methods to improve image quality and the second involves extracting features on the basis of color, texture and shape. Deep CNN architectures like VGG19, ResNet, and InceptionV3 are used to enhance the performance of classification. Moreover, ensemble learning techniques such as bagging, boosting and voting systems are added to increase the robustness and generalization of the models. An integrated deep learning system that is based on both feature-based and end-to-end learning is also suggested to enhance the accuracy of disease classification. The experiment is done on the PlantVillage dataset and the performance has been evaluated based on metrics like accuracy, precision, recall, and Area Under the Curve (AUC). The results are in the form of tabular comparisons and graphs to demonstrate the viability of the different models. The paper ends by pointing out that hybrid and ensemble-based deep learning approaches are superior to realize higher accuracy and reliable disease detection.