Enhancing Sustainable Agriculture: Detection of Plant Leaf Diseases Using YOLO-Based Object Detection

Suksun Promboonruang; Thummarat Boonrod; Bancha Luaphol; Sayun Phansomboon; Udom Wongsupha; Anucha Puthikulsakhon; Patcharin Zatun; Chaiwat Hmokaew; Kamonwan  Ratchatawetchakul

Authors

Suksun Promboonruang Kalasin University, Thailand
Thummarat Boonrod Kalasin University, Thailand
Bancha Luaphol Kalasin University, Thailand
Sayun Phansomboon Kalasin University, Thailand
Udom Wongsupha Kalasin University, Thailand
Anucha Puthikulsakhon Kalasin University, Thailand
Patcharin Zatun Kalasin University, Thailand
Chaiwat Hmokaew Kalasin University, Thailand
Kamonwan Ratchatawetchakul Kalasin University, Thailand

Keywords:

Plant Disease Detection, YOLO (You Only Look Once), Sustainable Agriculture

Abstract

This research explores the utilization of YOLO-based object detection for the early detection of plant leaf diseases, with the objective of promoting sustainable agricultural practices. This paper presents a comprehensive analysis of the YOLOv10s architecture, highlighting its advanced features designed to improve detection accuracy and computational efficiency. Our approach includes thorough data preparation, which entails splitting datasets into training, testing, and validation subsets, along with the implementation of hyperparameter optimization and pretraining-fine-tuning strategies. The findings indicate that YOLOv10s significantly surpasses earlier versions, achieving high detection accuracy while remaining viable for resource-constrained environments. Comparative analysis indicated that YOLOv9 outperformed in detecting healthy leaves (precision: 0.668, recall: 0.611) and leaf spot disease (precision: 0.632, recall: 0.626), whereas YOLOv10s exhibited a balanced performance in leaf blight detection. While YOLOv11 demonstrated incremental advancements, these were insufficient to justify its added complexity. This study highlights the transformative potential of deep learning technologies in agriculture, facilitating rapid disease detection and reducing reliance on chemical pesticides, thus supporting sustainable agricultural objectives.

References

Endo K, Hiraguri T, Kimura T, et al. Estimation of the amount of pear pollen based on flowering stage detection using deep learning. Sci Rep. 2024;14:13163. https://doi.org/10.1038/s41598-024-63611-w

Su J, Qin Y, Jia Z, et al. MPE-YOLO: enhanced small target detection in aerial imaging. Sci Rep. 2024;14:17799. https://doi.org/10.1038/s41598-024-68934-2

Khanam R, Hussain M. YOLOv11: An Overview of the Key Architectural Enhancements. arXiv preprint. 2024. Article arXiv:2410.17725.

Fan Z, Qin Z, Liu W, Chen M, Qiu Z. SS-YOLOv8: A Lightweight Algorithm for Surface Litter Detection. Appl Sci. 2024;14:9283. https://doi.org/10.3390/app14209283

Tang L, Li T, Xu C. Stratigraphic Division Method Based on the Improved YOLOv8. Appl Sci. 2024;14:9485. https://doi.org/10.3390/app14209485

Wang F, Yang X, Wei J. YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO. Appl Sci. 2024;14:9588. https://doi.org/10.3390/app14209588

Tayebi RM, Mu Y, Dehkharghanian T, et al. Automated bone marrow cytology using deep learning to generate a histogram of cell types. Commun Med. 2022;2:45. https://doi.org/10.1038/s43856-022-00107-6

Shen M, Liu Y, Chen J, et al. Defect detection of printed circuit board assembly based on YOLOv5. Sci Rep. 2024;14:19287. https://doi.org/10.1038/s41598-024-70176-1

Rajesh R, Manivannan PV. Automatic Traffic Sign, Animal Detection, and Recognition Using You Only Look Once to Avoid Human-Animal Road Conflicts. Eng Sci. 2024;31:1192-1203. https://doi.org/10.30919/es1192

Guo H, Wu T, Gao G, et al. Lightweight safflower cluster detection based on YOLOv5. Sci Rep. 2024;14:18579. https://doi.org/10.1038/s41598-024-69584-0

Lu Y, Lu X, Zheng L, Sun M, Chen S, Chen B, et al. Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems. Plants. 2024;13:972. https://doi.org/10.3390/plants13070972

Shiddiqi AM, Yogatama ED, Navastara DA. Resource-aware video streaming (RAViS) framework for object detection system using deep learning algorithm. MethodsX. 2023;11. https://doi.org/10.1016/j.mex.2023.102285

Sumit SS, Rambli DRA, Mirjalili S, Miah MSU, Ejaz MM. ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy. MethodsX. 2023;10. https://doi.org/10.1016/j.mex.2022.101936

Liu J, Wang X. Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods. 2020;16:83. https://doi.org/10.1186/s13007-020-00624-2

Guo J, Lou H, Chen H, et al. A new detection algorithm for alien intrusion on highway. Sci Rep. 2023;13:10667. https://doi.org/10.1038/s41598-023-37686-w

Bao W, Fan T, Hu G, et al. Detection and identification of tea leaf diseases based on AX-RetinaNet. Sci Rep. 2022;12:2183. https://doi.org/10.1038/s41598-022-06181-z

Avsar E, Feekings JP, Krag LA. Edge computing based real-time Nephrops (Nephrops norvegicus) catch estimation in demersal trawls using object detection models. Sci Rep. 2024;14:9481. https://doi.org/10.1038/s41598-024-60255-8

Yang D, Solihin MI, Ardiyanto I, et al. A streamlined approach for intelligent ship object detection using EL-YOLO algorithm. Sci Rep. 2024;14:15254. https://doi.org/10.1038/s41598-024-64225-y

He J, Chen H, Liu B, et al. Enhancing YOLO for occluded vehicle detection with grouped orthogonal attention and dense object repulsion. Sci Rep. 2024;14:19650. https://doi.org/10.1038/s41598-024-70695-x

Souza BJ, da Costa GK, Szejka AL, et al. A deep learning-based approach for axle counter in free-flow tolling systems. Sci Rep. 2024;14:3400. https://doi.org/10.1038/s41598-024-53749-y

Huang H, Tang X, Wen F, et al. Small object detection method with shallow feature fusion network for chip surface defect detection. Sci Rep. 2022;12:3914. https://doi.org/10.1038/s41598-022-07654-x

Zheng J, Liu H, He Q, et al. GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines. Sci Rep. 2024;14:15769. https://doi.org/10.1038/s41598-024-64991-9

Lu J, Yu M, Liu J. Lightweight strip steel defect detection algorithm based on improved YOLOv7. Sci Rep. 2024;14:13267. https://doi.org/10.1038/s41598-024-64080-x

Wu Y, Dong J, Chen J. YOLO-DCNet: A Semantic-Based Novel Flexible Lightweight Human Detection Algorithm. Int J Semant Web Inf Syst. 2024;20(1):1-23. https://doi.org/10.4018/IJSWIS.339000

Ahmed R, Abd-Elkawy EH. Improved Tomato Disease Detection with YOLOv5 and YOLOv8. Eng Technol Appl Sci Res. 2024;14(3):13922-13928. https://doi.org/10.48084/etasr.7262

S. Promboonruang, T. Boonrod, and Y. Ratchatawetchakul, “Efficient Waste Detection and Classification based on YOLOv5 Models”, Engineering Access, vol. 10, no. 1, pp. 51–58, Feb. 2024. https://doi.org/10.14456/mijet.2024.7