Enhanced Plant Disease Detection Using Computer Vision YOLOv11: Pre-Trained Neural Network Model Application

Muhammad Al Husaini; Agung  Rachmat Raharja; Vito Hafizh  Cahaya Putra; Hen Hen  Lukmana

doi:10.47709/cnahpc.v7i1.5146

Authors

Muhammad Al Husaini Siliwangi University
Agung Rachmat Raharja Bandung University
Vito Hafizh Cahaya Putra Satu University
Hen Hen Lukmana Siliwangi University

DOI:

10.47709/cnahpc.v7i1.5146

Keywords:

Deep Learning, Plant Disease Detection, Yolov11, Precision, Performance

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Abstract

This study investigates the application of YOLOv11, a cutting-edge deep learning model, to enhance the detection of plant diseases. Leveraging a comprehensive dataset of 737 images depicting tomato leaves affected by various diseases, YOLOv11 was trained and evaluated on key performance metrics such as precision, recall, and mAP. Experimental results the model was trained and evaluated on key metrics including accuracy (75.6%), precision (0.80), recall (0.77), and mAP@0.5 (75.6%). Experimental through base architectural such as enhanced feature extraction with C2 modules, improved multi-scale detection using SPPF layers, and optimized non-maximum suppression techniques. These improvements enable the model to achieve stable precision and recall for each class, even in challenging scenarios with overlapping objects and diverse environmental conditions. By addressing practical usability challenges, this system offers a scalable, accessible, and impactful solution for precision agriculture, paving the way for sustainable with this pretrained model. This study underscores the potential of deep learning-based models, particularly YOLOv11, in transforming the way monitoring and disease management are approached, demonstrating its ability to stable accuracy and operational efficiency in real-world applications. Furthermore, the practical usability of the YOLOv11-based system addresses challenges in the domain of precision plant detection desease. By providing a scalable, accessible, and highly efficient solution, the model offering a significant advancement toward sustainable agricultural practices.

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References

Aldakheel, E. A., Zakariah, M., & Alabdalall, A. H. (2024). Detection and identification of plant leaf diseases using YOLOv4. Frontiers in Plant Science, 15(April), 1–22. https://doi.org/10.3389/fpls.2024.1355941

Ali, M. L., & Zhang, Z. (2024a). The YOLO Framework?: A Comprehensive Review of Evolution , Applications , and Benchmarks in Object Detection. October. https://doi.org/10.20944/preprints202410.1785.v1

Ali, M. L., & Zhang, Z. (2024b). The YOLO Framework?: A Comprehensive Review of Evolution , Applications , and Benchmarks in Object Detection. December. https://doi.org/10.3390/computers13120336

Alif, M. A. R., & Hussain, M. (2024). YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain. 1–31. http://arxiv.org/abs/2406.10139

Bouni, M., Hssina, B., Douzi, K., & Douzi, S. (2023). Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction. Journal of Electrical and Computer Engineering, 2023. https://doi.org/10.1155/2023/5051005

Cahaya Putra, V. H., M.Al-Husaini, W., A., & Al, A. R. R. (2025). Design of an Intelligent Monitoring System Based on IoT with Random Forest Regression Algorithm for Height Detection in Cherry Tomato Plants Perancangan Sistem Monitoring Cerdas Berbasis IoT dengan Algoritma Random Forest Regression untuk Deteksi Ketinggi. 5(January), 1–16.

Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243–9275. https://doi.org/10.1007/s11042-022-13644-y

Fahim-Ul-Islam, M., Chakrabarty, A., Ahmed, S. T., Rahman, R., Kwon, H. H., & Jalil Piran, M. (2024). A Comprehensive Approach Toward Wheat Leaf Disease Identification Leveraging Transformer Models and Federated Learning. IEEE Access, 12(August), 109128–109156. https://doi.org/10.1109/ACCESS.2024.3438544

Huang, R., Pedoeem, J., & Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2503–2510. https://doi.org/10.1109/BigData.2018.8621865

Jeger, M., Beresford, R., Bock, C., Brown, N., Fox, A., Newton, A., Vicent, A., Xu, X., & Yuen, J. (2021). Global challenges facing plant pathology: multidisciplinary approaches to meet the food security and environmental challenges in the mid-twenty-first century. CABI Agriculture and Bioscience, 2(1), 1–18. https://doi.org/10.1186/s43170-021-00042-x

Jegham, N., Koh, C. Y., Abdelatti, M., & Hendawi, A. (2024). Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors. 1–20. http://arxiv.org/abs/2411.00201

Khanam, R., & Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. 2024, 1–9. http://arxiv.org/abs/2410.17725

Manjula, K., Spoorthi, S., Yashaswini, R., & Sharma, D. (2022). Plant Disease Detection Using Deep Learning. Lecture Notes in Electrical Engineering, 783, 1389–1396. https://doi.org/10.1007/978-981-16-3690-5_133

Mathew, M. P., & Mahesh, T. Y. (2022). Leaf-based disease detection in bell pepper plant using YOLO v5. Signal, Image and Video Processing, 16(3), 841–847. https://doi.org/10.1007/s11760-021-02024-y

Mohyuddin, G., Khan, M. A., Haseeb, A., Mahpara, S., Waseem, M., & Saleh, A. M. (2024). Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review. IEEE Access, 12(April), 60155–60184. https://doi.org/10.1109/ACCESS.2024.3390581

Paramanandham, N., Sundhar, S., & Priya, P. (2024). Enhancing Disease Detection with Weight Initialization and Residual Connections Using LeafNet for Groundnut Leaf Diseases. IEEE Access, 12(June), 91511–91526. https://doi.org/10.1109/ACCESS.2024.3422311

Perkasa, M. A. P., Akbar, R. R. El, Al Husaini, M., & Rizal, R. (2024). Visual Entity Object Detection System in Soccer Matches Based on Various Yolo Architecture. Jurnal Teknik Informatika (JUTIF), 5(3), 811–820. https://doi.org/10.52436/1.jutif.2024.5.3.2015

Ristaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V., Finegold, C., Garrett, K. A., Gilligan, C. A., Jones, C. M., Martin, M. D., MacDonald, G. K., Neenan, P., Records, A., Schmale, D. G., Tateosian, L., & Wei, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proceedings of the National Academy of Sciences of the United States of America, 118(23), 1–9. https://doi.org/10.1073/pnas.2022239118

Sapkota, R., Meng, Z., Churuvija, M., Du, X., Ma, Z., & Karkee, M. (2024). Comprehensive Performance Evaluation of YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments. 1–27.

Soudeep, S., Mridha, M. F., Jahin, M. A., & Dey, N. (2024). DGNN-YOLO: Interpretable Dynamic Graph Neural Networks with YOLO11 for Small Object Detection and Tracking in Traffic Surveillance. http://arxiv.org/abs/2411.17251

Tanzib Hosain, M., Zaman, A., Abir, M. R., Akter, S., Mursalin, S., & Khan, S. S. (2024). Synchronizing Object Detection: Applications, Advancements and Existing Challenges. IEEE Access, 12(April), 54129–54167. https://doi.org/10.1109/ACCESS.2024.3388889

Wang, C.-Y., & Liao, H.-Y. M. (2024). YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems. October. https://doi.org/10.1561/116.20240058

Wang, H., Shang, S., Wang, D., He, X., Feng, K., & Zhu, H. (2022). Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model. Agriculture (Switzerland), 12(7). https://doi.org/10.3390/agriculture12070931

Youwai, S., & Chaiyaphat, A. (2024). Precision Road Infrastructure Management?: Monocular Vision-Based 3D Damage Detection and Assessment Precision Road Infrastructure Management?: Monocular Vision-Based 3D Damage Detection and Assessment. November.

Yuan, X., Yu, H., Geng, T., Ma, R., & Li, P. (2024). Enhancing sustainable Chinese cabbage production: a comparative analysis of multispectral image instance segmentation techniques. Frontiers in Sustainable Food Systems, 8(November), 1–18. https://doi.org/10.3389/fsufs.2024.1433701