Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

Autor:	Tariq M; Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan.; Department of Computer Science, Lahore Garrison University, Lahore, Pakistan., Ali U; Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea., Abbas S; College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia., Hassan S; Marine Engineering Department, Military Technological College, Muscat, Oman., Naqvi RA; Department of Artificial Intelligence and Robotics, Sejong University, Seoul, Republic of Korea., Khan MA; Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam, Republic of Korea., Jeong D; College of Convergence Engineering, Sangmyung University, Seoul, Republic of Korea.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2024 Jun 26; Vol. 15, pp. 1402835. Date of Electronic Publication: 2024 Jun 26 (Print Publication: 2024).
DOI:	10.3389/fpls.2024.1402835
Abstrakt:	The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Tariq, Ali, Abbas, Hassan, Naqvi, Khan and Jeong.)
Databáze:	MEDLINE
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