Method for lightweight tomato leaf disease recognition based on improved YOLOv11s
DOI:
https://doi.org/10.25165/ijabe.v18i5.9928Keywords:
YOLO, LWGANet, object detection, lightweight modelAbstract
Accurate detection of tomato leaf diseases is crucial for early prevention and ensuring agricultural production. This study addresses six tomato leaf diseases: bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, and yellow leaf curl virus. A lightweight detection model, YOLO-LGS, is proposed to achieve efficient and automated disease detection. The dataset of tomato leaf diseases was first augmented to enrich the disease features, thereby improving the model’s detection performance. The YOLO-LGS model is built on the YOLOv11 architecture, incorporating lightweight group attention net (LWGANet) to reconstruct the backbone network, replacing the convolutional block with parallel spatial attention mechanism with the grouped channel-wise self-attention (GCSA) mechanism, and introducing separated and enhanced attention module (SEAM) into the detection head to balance performance and efficiency. Experimental results show that the YOLO-LGS model achieves an mAP50 of 0.693 and an F1 score of 0.677, outperforming other YOLO models (YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s). Additionally, the model’s parameter size is only 6.333 M, and its GFLOPs is 13.4, representing reductions of 32.739% and 37.089%, respectively, compared to YOLOv11s, significantly lowering computational cost while maintaining detection performance. The results demonstrate the effectiveness of LWGANet, GCSA, and SEAM. The development of the YOLO-LGS model provides an efficient, lightweight solution for tomato leaf disease detection in resource-constrained environments. Keywords: YOLO, LWGANet, object detection, lightweight model DOI: 10.25165/j.ijabe.20251805.9928 Citation: Chen D X, Liu L, Zhao L, Shi Y, Wang F, Meng D, et al. Method for lightweight tomato leaf disease recognition based on improved YOLOv11s. Int J Agric & Biol Eng, 2025; 18(5): 298–305.References
Zhang S L, Wang J Z, Yang K, Guan M L. YOLO-ACT: An adaptive cross-layer integration method for apple leaf disease detection. Frontiers in Plant Science, 2024; 15: 1451078.
Sajitha P, Andrushia A D, Anand N, Naser M Z. A review on machine learning and deep learning image-based plant disease classification for industrial farming systems. Journal of Industrial Information Integration, 2024; 38: 100572.
Liu W B, Bai C H, Tang W, Xia Y, Kang J. A Lightweight Real-Time recognition algorithm for tomato leaf disease based on improved YOLOv8. Agronomy, 2024; 14（9）: 2069.
Tang Z W, He X Y, Zhou G X, Chen A B, Wang Y F, Li L J, et al. A precise image-based tomato leaf disease detection approach using PLPNet. Plant Phenomics, 2023; 5: 0042.
Wang Y R, Wang Y H, Mu J, Raza Mustafa G, Wu Q J, Wang Y, et al. Enhanced multiscale plant disease detection with the PYOLO model innovations. Scientific Reports, 2025; 15（1）: 5179.
Too E C, Li Y J, Njuki S, Liu Y C. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 2019; 161: 272–279.
Lin Y S, Huang Z F, Liang Y, Liu Y F, Jiang W P. AG-YOLO: A rapid citrus fruit detection algorithm with global context fusion. Agriculture, 2024; 14（1）: 114.
Lin P, Lee W S, Chen Y M, Peres N, Fraisse C. A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field. Precision Agriculture, 2020; 21: 387–402.
Chu P Y, Li Z J, Lammers K, Lu R F, Liu X M. Deep learning-based apple detection using a suppression mask R-CNN. Pattern Recognition Letters, 2021; 147: 206–211.
Behera S K, Rath A K, Sethy P K. Fruits yield estimation using Faster R-CNN with MIoU. Multimedia Tools and Applications, 2021; 80: 19043–19056.
Hua S, Xu M J, Xu Z F, Ye H B, Zhou C Q. Multi-feature decision fusion algorithm for disease detection on crop surface based on machine vision. Neural Computing and Applications, 2022; 34: 9471–9484.
Deng F M, Mao W, Zeng Z Q, Zeng H, Wei B Q. Multiple diseases and pests detection based on federated learning and improved faster R-CNN. IEEE Transactions on Instrumentation and Measurement, 2022; 71: 1–11.
Alruwaili M, Siddiqi M H, Khan A, Azad M, Khan A, Alanazi S. RTF-RCNN: An architecture for real-time tomato plant leaf diseases detection in video streaming using Faster-RCNN. Bioengineering, 2022; 9（10）: 565.
Kaur P, Harnal S, Gautam V, Singh M P, Singh S P. An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. Engineering Applications of Artificial Intelligence, 2022; 115: 105210.
Xie Z D, Yang Z, Li C, Zhang Z, Jiang J Z, Guo H Y. YOLO-Ginseng: a detection method for ginseng fruit in natural agricultural environment. Frontiers in Plant Science, 2024; 15: 1422460.
Sun C L, Azman A B, Wang Z Y, Gao X X, Ding K. YOLO-UP: A high-throughput pest detection model for dense cotton crops utilizing UAV-Captured visible light imagery. IEEE Access, 2025; 13: 19937–19945.
Liu Z Y, Zhuo L, Dong C W, Li J F. YOLO-TBD: tea bud detection with triple-branch attention mechanism and self-correction group convolution. Industrial Crops and Products, 2025; 226: 120607.
Wang J X, Liu M, Du Y R, Zhao M H, Jia H L, Guo Z, et al. PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning. Engineering Applications of Artificial Intelligence, 2024; 134: 108700.
Chen J Q, Wang H B, Zhang H D, Luo T, Wei D P, Long T, et al. Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion. Computers and Electronics in Agriculture, 2022; 202: 107412.
Wang X W, Liu J. An efficient deep learning model for tomato disease detection. Plant Methods, 2024; 20: 61.
Kang R, Huang J X, Zhou X H, Ren N, Sun S P. Toward real scenery: a lightweight tomato growth inspection algorithm for leaf disease detection and fruit counting. Plant Phenomics, 2024; 6: 0174.
Lin J W, Hu G, Chen J. Mixed data augmentation and osprey search strategy for enhancing YOLO in tomato disease, pest, and weed detection. Expert Systems with Applications, 2025; 264: 125737.
Xu Y L, Chen Q Y, Kong S L, Xing L, Wang Q, Cong X, et al. Real-time object detection method of melon leaf diseases under complex background in greenhouse. Journal of Real-Time Image Processing, 2022; 19: 985–995.
Wang J L, Qin C C, Hou B B, Yuan Y, Zhang Y K, Feng W F. LCGSC-YOLO: A lightweight apple leaf diseases detection method based on LCNet and GSConv module under YOLO framework. Frontiers in plant science, 2024; 15: 1398277.
Khanam R, Hussain M. Yolov11: An overview of the key architectural enhancements. arXiv: 2410.17725, 2024; In press. doi: 10.48550/arXiv.2410.17725.
Khan A T, Jensen S M. LEAF-Net: A unified framework for leaf extraction and analysis in multi-crop phenotyping using YOLOv11. Agriculture, 2025; 15（2）: 196.
Teng H X, Wang Y D, Li W T, Chen T, Liu Q H. Advancing rice disease detection in farmland with an enhanced YOLOv11 algorithm. Sensors, 2025; 25（10）: 3056.
Meng H T, Tao M, Huang R D, Xu Y Q, Memon M B. Improved YOLOv11-EGM deep learning model for rock fragment identification. Archives of Civil and Mechanical Engineering, 2025; 25: 223.
Li Y B, Zhang W W, Lyu S T, Yu J, Ge D D, Guo J W, et al. YOLOv11-CAFM model in ground penetrating radar image for pavement distress detection and optimization study. Construction and Building Materials, 2025; 485: 141907.
Weng T H, Niu X P. Enhancing UAV object detection in low-light conditions with ELS-YOLO: A lightweight model based on improved YOLOv11. Sensors, 2025; 25（14）: 4463.
Lu W, Chen S B, Ding C H Q, Tang J, Luo B. LWGANet: a lightweight group attention backbone for remote sensing visual tasks. arXiv: 2501.10040, 2025; In press. doi: 10.48550/arXiv.2501.10040.
Li J Y, Zhang Z L, Zuo W M. TBSN: Transformer-based blind-spot network for self-supervised image denoising. arXiv: 2404.07846, 2024; In press. doi: 10.48550/arXiv.2404.07846.
Yu Z P, Huang H B, Chen W J, Su Y X, Liu Y H, Wang X Y. Yolo-facev2: A scale and occlusion aware face detector. Pattern Recognition, 2024; 155: 110714.
Guan S T, Lin Y M, Lin G Y, Su P S, Huang S L, Meng X Y, et al. Real-time detection and counting of wheat spikes based on improved YOLOv10. Agronomy, 2024; 14（9）: 1936.
Gui J S, Wu J C, Wu D W, Chen J N, Tong J H. A lightweight tea buds detection model with occlusion handling. Journal of Food Measurement and Characterization, 2024; 18: 7533–7549.
Kaur R, Mittal U, Wadhawan A, Almogren A, Singla, J, Bharany S, et al. YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation. Scientific Reports, 2025; 15: 28513.
Ayyad S M, Sallam N M, Gamel S A, Ali Z H. Particle swarm optimization with YOLOv8 for improved detection performance of tomato plants. Journal of Big Data, 2025; 12: 152.
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