Root-YOLOv7: Multi-scale adaptive object detection and grading of root-knot nematode disease
DOI:
https://doi.org/10.25165/ijabe.v18i2.9414Keywords:
object detection, YOLOv7, root-knot nematode disease, deep learning, disease gradingAbstract
Root-knot nematodes can infect over 2000 plants, which causes significant economic losses. Rapid and accurate detection of root-knot nematode disease is the key to screening resistant varieties and evaluating the effect of prevention and control. To address the challenge of detecting root-knot nematode disease caused by dense root knots, multiple fibrous roots, and small root knots, this paper takes cucumber as the research object to propose a detection and grading model Root-YOLOv7. Specifically, the backbone network of YOLOv7 is restructured, which enables the proposed model to better capture object information at different scales through a hierarchical structure and a self-attention module based on shifted windows. Additionally, combined with the Wise-IoU loss function, the proposed model can adaptively adjust the weight of the overlapping part of the object box, which solves the problem that bounding box regression cannot be optimized effectively when detecting low-quality objects. Furthermore, an improved head network structure is proposed to compute the attention weights by capturing the cross-dimension interaction of the root knot feature between the spatial and channel dimensions. To verify the effectiveness of the proposed model, the performance of Root-YOLOv7 is compared with typical object detection models. Experimental results show that the AP@0.5 of Root-YOLOv7 reaches 87.40%, which is 72.67%, 5.60%, 5.28%, 9.68%, 5.83%, and 7.45% higher than Faster R-CNN, RT-DETR, YOLOv5, YOLOv6, YOLOv7, and YOLOv8, respectively. The proposed approach is expected to reduce the workload of plant pathologists and provide technical support for the cultivation of plant varieties with disease resistance. Key words: object detection; YOLOv7; root-knot nematode disease; deep learning; disease grading DOI: 10.25165/j.ijabe.20251802.9414 Citation: Zhao Y, Zhao H H, Xiong H T, Zhang F, Lu C, Li J. Root-YOLOv7: Multi-scale adaptive object detection and grading of root-knot nematode disease. Int J Agric & Biol Eng, 2025; 18(2): 259–268.References
Lapajne J, Knapič M, Žibrat U. Comparison of selected dimensionality reduction methods for detection of root-knot nematode infestations in potato tubers using hyperspectral imaging. Sensors (Basel), 2022; 22(1): 367.
Haque F, Thimmanagari M, Chiang Y W. Ultrasound assisted cyanotoxin extraction for nematode inhibition in soil. Ultrason Sonochem, 2022; 89: 106120.
Atkinson H J, Lilley C J, Urwin P E. Strategies for transgenic nematode control in developed and developing world crops. Curr Opin Biotechnol, 2012; 23(2): 251–256.
Park E, Kim Y-S, Faqeerzada M A, Kim M S, Baek I, Cho B-K. Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer). Front Plant Sci, 2023; 14: 1109060.
Abade A, Ferreira P A, Vidal F D. Plant diseases recognition on images using convolutional neural networks: A systematic review. Comput Electron Agric, 2021; 185: 106125.
Hu X L, Liu Y, Zhao Z X, Liu J T, Yang X T, Sun C H, et al. Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Comput Electron Agric, 2021; 185: 10613.
Ma G J, Xu S P, Yang T, Du Z B, Zhu L M, Ding H, et al. A transfer learning-based method for personalized state of health estimation of lithium-ion batteries. IEEE Trans Neural Networks Learn Syst, 2024; 35(1): 759–769.
Tian L L, Wang Z D, Liu W B, Cheng Y H, Alsaadi F E, Liu X H. An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing. Complex Intell Syst, 2022; 8(1): 467–476.
Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal, 2019; 58: 101552.
Deng L M, Li J, Han Z Z. Online defect detection and automatic grading of carrots using computer vision combined with deep learning methods. LWT-Food Science and Technology, 2021; 149: 111832.
Xiong H T, Xiao Y, Zhao H P, Xuan K, Zhao Y, Li J. AD-YOLOv5: An object detection approach for key parts of sika deer based on deep learning. Comput Electron Agric, 2024; 217: 108610.
Bohnenkamp D, Behmann J, Paulus S, Steiner U, Mahlein A K. A hyperspectral library of foliar diseases of wheat. Phytopathology, 2021; 111(9): 1583–1593.
Gomez-Caro S, Mendoza-Vargas L A, Ramirez-Gil J G, Burbano-David D, Soto-Suarez M, Melgarejo L M. Close-Range Thermography and reflectance spectroscopy support in vitro and in vivo characterization of colletotrichum spp. isolates from mango fruits. Plant Disease, 2022; 106(9): 2355–2369.
Mohammadpoor M, Nooghabi M G, Ahmedi Z. An intelligent technique for grape fanleaf virus detection. Int J Interact Multimedia Artif Intell, 2020; 6(1): 62–67.
Almadhor A, Rauf H T, Lali M I U, Damasevicius R, Alouffi B, Alharbi A. AI-driven framework for recognition of guava plant diseases through machine learning from DSLR camera sensor based high resolution imagery. Sensors. 2021; 21(11). doi: 10.3390/s21113830.
Ostovar A, Talbot B, Puliti S, Astrup R, Ringdahl O. Detection and classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce using RGB images and machine learning. Sensors (Basel), 2019; 19(7). doi: 10.3390/s19071579.
Huang W X, Huo Y, Yang S C, Liu M J, Li H, Zhang M. Detection of Laodelphax striatellus (small brown planthopper) based on improved YOLOv5. Comput Electron Agric, 2023; 206: 107657.
Li K, Zhang L, Li B, Li S, Ma J. Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity. Plant Methods, 2022; 18(1): 109.
Wang F Y, Rao Y, Luo Q, Jin X, Jiang Z H, Zhang W, et al. Practical cucumber leaf disease recognition using improved Swin Transformer and small sample size. Comput Electron Agric, 2022; 199: 107163.
Wang T W, Zhao L G, Li B H, Liu X W, Xu W K, Li J. Recognition and counting of typical apple pests based on deep learning. Ecol Inf, 2022; 68. doi: 10.1016/j.ecoinf.2022.101556.
Liu J, Wang X W, Miao W Q, Liu G X. Tomato Pest Recognition Algorithm Based on Improved YOLOv4. Front Plant Sci, 2022; 13. doi: 10.3389/fpls.2022.814681.
Qi J T, Liu X N, Liu K, Xu F R, Guo H, Tian X L, et al. An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Comput Electron Agric, 2022; 194: 106780.
Qing X, Wang Y H, Lu X Q, Li H B, Wang X, Li H M, et al. NemaRec: A deep learning-based web application for nematode image identification and ecological indices calculation. Eur J Soil Biol, 2022; 110. doi: 10.1016/j.ejsobi.2022.103408.
Sharma V, Tripathi A K, Mittal H. DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection. Ecol Inf, 2023; 75. doi: 10.1016/j.ecoinf.2023.102025.
Wang C S, Sun S D, Zhao C J, Mao Z C, Wu H R, Teng G F. A detection model for cucumber root-knot nematodes based on modified YOLOv5-CMS. Agronomy-Basel. 2022; 12(10). doi: 10.3390/agronomy12102555.
Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning. Journal of Big Data, 2019; 6(1). doi: 10.1186/s40537-019-0197-0.
Gai W, Liu Y, Zhang J, Jing G. An improved Tiny YOLOv3 for real-time object detection. Systems Science & Control Engineering, 2021; 9(1): 314–321.
Zeng N, Wu P, Wang Z, Li H, Liu W, Liu X. A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection. IEEE Trans Instrum Meas, 2022; 71: 1–14.
Wang C-Y, Bochkovskiy A, Liao H Y M . YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022; pp.7464–7475.
Soeb M J A, Jubayer M F, Tarin T A, Al Mamun M R, Ruhad F M, Parven A, et al. Tea leaf disease detection and identification based on YOLOv7 (YOLO-T). Sci Rep. 2023; 13(1). doi: 10.1038/s41598-023-33270-4.
Bochkovskiy A , Wang C-Y, Liao H Y M. YOLOv4: Optimal speed and accuracy of object detection. arXiv - CS - Computer Vision and Pattern Recognition, 2020. doi: 10.48550/arXiv.2004.10934.
Chen Y, Shu H C, Xu W J, Yang Z Y, Hong Z H, Dong M S. Transformer text recognition with deep learning algorithm. Comput Commun. 2021; 178: 153–60. doi: 10.1016/j.comcom.2021.04.031.
Chen X, Yuan M J H, Yang Q, Yao H Y, Wang H Y. Underwater-YCC: Underwater target detection optimization algorithm based on YOLOv7. J Mar Sci Eng. 2023; 11(5). doi: 10.3390/jmse11050995.
Tong Z J, Chen Y H, Xu Z W, Yu R. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. arXiv - CS - Computer Vision and Pattern Recognition, 2023. doi: 10.48550/arXiv-2301.10051.
Huang H Q, Lan G W, Wei J, Zhong Z, Xu Z R, Li D B, et al. TLI-YOLOv5: A lightweight object detection framework for transmission line inspection by unmanned aerial vehicle. Electronics, 2023; 12(15). doi: 10.3390/electronics12153340.
Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017; 39(6): 1137–1149.
Lv W Y, Xu S L, Zhao Y A, Wang G Z, Wei J M, Cui C, Du Y N, et al. DETRs beat YOLOs on real-time object detection. arXiv - CS - Computer Vision and Pattern Recognition, 2023. doi: 10.48550/arxiv-2304.08069.
Roy A M, Bhaduri J. DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism. Adv Eng Inf, 2023; 56. doi: 10.1016/j.aei.2023.102007.
Saydirasulovich S N, Abdusalomov A, Jamil M K, Nasimov R, Kozhamzharova D, Cho Y I. A YOLOv6-based improved fire detection approach for smart city environments. Sensors, 2023; 23(6): 3161.
Wang S Y, Wu D S, Zheng X Y. TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection. Front Plant Sci, 2023; 14. doi: 10.3389/fpls.2023.1223410.
Jiang H Y, Hu F, Fu X Q, Chen C R, Wang C, Tian L X, et al. YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor. Front Plant Sci, 2023; 14. doi: 10.3389/fpls.2023.1257947.
Yannong Xiao M W, Yanping Fu, Fantao Zeng. Comparison of grading methods for vegetable root knot nematode disease. Journal of Huazhong Agricultural University, 2000; 19(4): 336–338.
Althnian A, AlSaeed D, Al-Baity H, Samha A, Bin Dris A, Alzakari N, et al. Impact of dataset size on classification performance: an empirical evaluation in the medical domain. App.Sci-Basel, 2021; 11(2). doi: 10.3390/app11020796.
Xu W K, Zhao L G, Li J, Shang S Q, Ding X P, Wang T W. Detection and classification of tea buds based on deep learning. Comput Electron Agric, 2022; 192: 106547.
Downloads
Published
How to Cite
Issue
Section
License
IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
