A Prototype System for Detecting Land Snails in Experimental Vegetable Plot Using YOLOv10-X Deep Learning Model

Main Article Content

Anumat Klinom
Walailuck Wongruen

Abstract

This paper presents a prototype system for detecting land snails in an experimental vegetable plot using YOLOv10-X Deep learning model. In this proposed system, (Internet of Things) devices were used in conjunction with computer vision techniques and YOLOv10-X Deep Learning model. Users can view the results of land snail detection in real time, view land snail detection reports via mobile application, and receive land snail detection notifications via the LINE application. The land snail species used in this study were the African giant snail and the Siamese snail. From random testing of the detection accuracy in 100 video image frames, it was found that the proposed system gave an accuracy of 74.15% for detecting African giant snails, 64.08% for Siamese snails, and mean average precision (mAP) of both classes was 97.31%.

Article Details

How to Cite
Klinom, A., & Wongruen, W. (2026). A Prototype System for Detecting Land Snails in Experimental Vegetable Plot Using YOLOv10-X Deep Learning Model. Journal of Science Ladkrabang, 35(1), 69–92. retrieved from https://li01.tci-thaijo.org/index.php/science_kmitl/article/view/266951
Section
Research article

References

Ahmad, I., Yang, Y., Yue, Y., Ye, C., Hassan, M., Cheng, X., Wu, Y., & Zhang, Y. (2022). Deep learning based detector YOLOv5 for identifying insect pests. Applied Sciences, 12(19), Article 10167. https://doi.org/10.3390/app121910167

Ansari, S. (2023). Building computer vision applications using artificial neural networks: With examples in OpenCV and TensorFlow with Python (2nd ed.). Apress.

Bento, J., Paixão, T., & Alvarez, A. B. (2025). Performance evaluation of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for stamp detection in scanned documents. Applied Sciences, 15(6), Article 3154. https://doi.org/10.3390/app15063154

Dumidae, A., Janthu, P., Subkrasae, C., Pumidonming, W., Dekumyoy, P., Thanwisai, A., & Vitta, A. (2020). Genetic analysis of Cryptozona Siamensis (stylommatophora, ariophantidae) populations in Thailand using the mitochondrial 16S rRNA and COI sequences. PLOS ONE, 15(9), Article e0239264. https://doi.org/10.1371/journal.pone.0239264

Hussain, M., & Khanam, R. (2024). In-depth review of YOLOv1 to YOLOv10 variants for enhanced photovoltaic defect detection. Solar, 4(3), 351-386. https://doi.org/10.3390/solar4030016

Kongim, B. (2008). Land snails: invertebrates that play a role in human life. Advanced Science Journal, 2, 1-6. (in Thai)

Kumar, P. (2020). A review-on molluscs as an agricultural pest and their control. International Journal of Food Science and Agriculture, 4(4), 383-389. https://doi.org/10.26855/ijfsa.2020.12.004

Li, Q., & Qiu, L. (2024). A snail species identification method based on deep learning in food safety. Mathematical Biosciences and Engineering, 21(3), 3652-3667. https://doi.org/10.3934/mbe.2024161

Lu, Y., Zhang, L., & Xie, W. (2020). YOLO-compact: An efficient YOLO network for single category real-time object detection. Proceedings of the 2020 Chinese Control and Decision Conference (CCDC) (pp. 1931-1936). IEEE. https://doi.org/10.1109/CCDC49329.2020.9164580

Nelson, S. (2012). Injuries caused by the giant African snail to papaya [Report No. MP-6]. University of Hawaii, College of Tropical Agriculture and Human Resources. https://www.ctahr.hawaii.edu/oc/freepubs/pdf/MP-6.pdf

Panha, S., Sutcharit, C., Tongkerd, P., & Naggs, F. (2009, June). An illustrated guide to Thai land snails. NSTDA. https://www.nstda.or.th/brt/images/book/146.pdf (in Thai)

Poonkuntran, S., Dhanraj, R. K., & Balusamy, B. (2022). Object detection with deep learning models: Principles and applications (1st ed.). Chapman & Hall/CRC Press.

Rintarak, D., Karnchananitipat, N., Iamsuwansuk, A., & Kaewta, S. (2017). Species diversity of terrestrial pest snails in agricultural ecosystem and environment in Thailand [Annual Research Report]. Plant Protection Research and Development Office, Department of Agriculture. (in Thai)

Sikka, B. (2021). Elements of deep learning for computer vision (1st ed.). BPB Publications.

Siriwechviriya, P. (2020). The application of loop-mediated isothermal amplification technique for epidemic detection of angiostrongylus cantonensis in land snails. Thai Journal of Public Health, 50(1), 37-46.

Sutcharit C., & Panha S. (2008). Land snails in Khao Nan National Park (1st ed.). Biodiversity Research and Training Program (BRT Project), Bangkok. (in Thai)

Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-time end-to-end object detection. arXiv, Article 2405.14458. https://arxiv.org/abs/2405.14458

Wang, Z., Lee, I., Tie, Y., Cai, J., & Qi, L. (2018). Real-world field snail detection and tracking. Proceedings of the 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1703-1708). IEEE. https://doi.org/10.1109/ICARCV.2018.8581271