Abstract

The classification of plant leaf diseases is critical for ensuring agricultural productivity and sustainability. Recent improvements in deep learning algorithms have shown a lot of promise for correctly identifying and diagnosing plant diseases by looking at images of leaves. To address the challenge of plant leaf disease classification using deep learning algorithms is critical for minimizing agricultural losses. The primary objective of this comparative analysis is to evaluate the effectiveness of various deep learning algorithms in classifying plant leaf diseases. To contribute to the development of a user-friendly classification tool that can be utilized by farmers and agricultural professionals, thus promoting early disease detection and intervention. The primary goal is to identify the most accurate and robust algorithm for classifying plant leaf diseases using images. To evaluate several prominent deep learning models, including Convolutional Neural Networks (CNNs), Median-Modified Wiener Filter (MMWF) reduces noise and enhances image quality, improving feature preservation for plant leaf classification. Hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN) enhances feature extraction and segmentation, improving disease detection accuracy in plant leaves. It enables robust comparative analysis against other deep learning models, optimizing classification performance. Southern Leaf Blight (SLB) serves as a critical case study in deep learning for plant disease classification, highlighting model accuracy, feature extraction, and real-time diagnosis in agricultural applications. The test results show that the suggested method works better than current ones, and it got an F1-score of 92%, an accuracy of 95%, a precision of 92%, a recall of 90%, and a recall of 90%. The programming language Python was used to create the model. Future research in plant leaf disease classification using deep learning could explore hybrid models that combine multiple algorithms for improved accuracy.

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