Automatic detection of tomato leaf disease using an adopted deep learning algorithm

Article type: Research Article

Authors: Guo, Xu^{; *}

Affiliations: College of Electronics and Information, Shanghai Dianji University, Shanghai, China

Correspondence: [*] Corresponding author. Xu Guo, College of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China. E-mail:guox@sdju.edu.cn.

Abstract: The detection of tomato leaf diseases is crucial for agricultural sustainability, impacting crop health, yield optimization, and global food supply. Despite the advancements in deep learning methods, a pressing challenge persists— achieving consistently high accuracy rates, particularly in the context of rigorous agricultural requirements. This study addresses this problem directly, introducing a novel approach by employing the Yolov8 architecture in a deep learning model for tomato leaf disease detection. The identified research challenge is precisely targeted, and the model is developed using a meticulously curated custom dataset. Through comprehensive training, validation, and testing phases, the study ensures the robust performance of the Yolov8 model. The novelty of this research lies in its focused solution to the specific accuracy challenge within deep learning-based tomato leaf disease detection. The proposed methodology is rigorously evaluated through extensive experimentation, showcasing its ability to surpass existing benchmarks and offering a highly effective solution. This innovative approach not only contributes a unique solution to the identified problem but also advances the field by providing a more accurate and reliable method for detecting tomato leaf diseases.

Keywords: Tomato leaf disease detection, deep learning methods, agricultural sector

DOI: 10.3233/JIFS-236905

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7909-7921, 2024