Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Promila Ghosh, Amit Kumar Mondal, Sajib Chatterjee, Mehedi Masud, Hossam Meshref and Anupam Kumar Bairagi ()
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Promila Ghosh: Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Amit Kumar Mondal: Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Sajib Chatterjee: Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Mehedi Masud: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Hossam Meshref: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Anupam Kumar Bairagi: Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh

Mathematics, 2023, vol. 11, issue 10, 1-24

Abstract: Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.

Keywords: sunflower diseases; transfer learning; deep learning; hybrid model; explainable AI; LIME (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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