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

被引:0
|
作者
Tariq, Maria [1 ,2 ]
Ali, Usman [3 ]
Abbas, Sagheer [4 ]
Hassan, Shahzad [5 ]
Naqvi, Rizwan Ali [6 ]
Khan, Muhammad Adnan [7 ]
Jeong, Daesik [8 ]
机构
[1] Natl Coll Business Adm & Econ, Dept Comp Sci, Lahore, Pakistan
[2] Lahore Garrison Univ, Dept Comp Sci, Lahore, Pakistan
[3] Sejong Univ, Dept Comp Sci & Engn, Seoul, South Korea
[4] Prince Mohammad Bin Fahd Univ, Coll Comp Engn & Sci, Al Khobar, Saudi Arabia
[5] Mil Technol Coll, Marine Engn Dept, Muscat, Oman
[6] Sejong Univ, Dept Artificial Intelligence & Robot, Seoul, South Korea
[7] Gachon Univ, Fac Artificial Intelligence & Software, Dept Software, Seongnam, South Korea
[8] Sangmyung Univ, Coll Convergence Engn, Seoul, South Korea
来源
基金
新加坡国家研究基金会;
关键词
intelligent agriculture system; machine learning (ML); corn leaf disease; explainable artificial intelligence (XAI); Visual Geometry Group 16 (VGG16); layer-wise relevance propagation (LRP);
D O I
10.3389/fpls.2024.1402835
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
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.
引用
收藏
页数:12
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