Abstract
Timely and effective locust detection to prevent locust plagues is crucial for safeguarding agricultural production and ecological balance. However, under natural conditions, the “colour mixing mechanism” of locusts and the small scale of locusts in high-resolution images make it difficult to detect locusts. In this study, we propose a multi-scale prediction network SSK-Yolo based on YoloV5 to effectively solve the above two problems. Firstly, in the data preprocessing stage, in order to better adapt to the relatively small-scale targets, we use the k-means algorithm to cluster the a priori frames to obtain anchor frames with appropriate scale sizes. Secondly, in the backbone, we still use the traditional convolution to extract the shallow graphical features, and we use swin-transformer to extract the deep semantic features, so as to improve the accuracy of feature extraction and fusion for small targets in high-resolution images. In addition, in the data post-processing stage, we replace the NMS algorithm with the soft-nms algorithm by setting a Gaussian function for the neighbouring detection frames based on the overlapping part instead of suppressing all of them. A series of experimental results on the publicly available East Asian locust dataset demonstrate that SSK-Yolo outperforms YoloV5 with a 5% improvement in precision, 1.64% in recall, 12% in mAP, and 2.66% in F1-score. SSK-Yolo provides an efficient and viable solution for locust detection in the field of pest and disease control.
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References
Ahmad Loti, N.N., Mohd Noor, M.R., Chang, S.W.: Integrated analysis of machine learning and deep learning in chili pest and disease identification. J. Sci. Food Agric. 101(9), 3582–3594 (2021)
Al Bashish, D., Braik, M., Bani-Ahmad, S.: A framework for detection and classification of plant leaf and stem diseases. In: 2010 International Conference on Signal and Image Processing, pp. 113–118. IEEE (2010)
Aurangzeb, K., Akmal, F., Attique Khan, M., Sharif, M., Javed, M.Y.: Advanced machine learning algorithm based system for crops leaf diseases recognition. In: 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), pp. 146–151 (2020). https://doi.org/10.1109/CDMA47397.2020.00031
Chen, C., Liang, Y., Zhou, L., Tang, X., Dai, M.: An automatic inspection system for pest detection in granaries using yolov4. Comput. Electron. Agric. 201, 107302 (2022)
Chudzik, P., Mitchell, A., Alkaseem, M., Wu, Y., Fang, S., Hudaib, T., Pearson, S., Al-Diri, B.: Mobile real-time grasshopper detection and data aggregation framework. Sci. Rep. 10(1), 1150 (2020)
Dong, S., et al.: Automatic crop pest detection oriented multiscale feature fusion approach. Insects 13(6), 554 (2022)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)
Gong, H., et al.: Based on FCN and DenseNet framework for the research of rice pest identification methods. Agronomy 13(2), 410 (2023)
Islam, M.A., Islam, M.S., Hossen, M.S., Emon, M.U., Keya, M.S., Habib, A.: Machine learning based image classification of papaya disease recognition. In: 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1353–1360 (2020). https://doi.org/10.1109/ICECA49313.2020.9297570
Jocher, G., et al.: ultralytics/yolov5: v6. 0-YOLOv5n ‘nano’ models, roboflow integration, tensorflow export, OpenCV DNN support. Zenodo (2021)
Karar, M.E., Alsunaydi, F., Albusaymi, S., Alotaibi, S.: A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alex. Eng. J. 60(5), 4423–4432 (2021)
Khalifa, N.E.M., Loey, M., Taha, M.H.N.: Insect pests recognition based on deep transfer learning models. J. Theor. Appl. Inf. Technol. 98(1), 60–68 (2020)
Li, W., Zhu, T., Li, X., Dong, J., Liu, J.: Recommending advanced deep learning models for efficient insect pest detection. Agriculture 12(7), 1065 (2022)
Liu, J., Wang, X.: Tomato diseases and pests detection based on improved yolo V3 convolutional neural network. Front. Plant Sci. 11, 898 (2020)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016, pp. 21–37. Springer International Publishing, Cham (2016)
Liu, W., Wu, G., Ren, F., Kang, X.: DFF-ResNet: an insect pest recognition model based on residual networks. Big Data Min. Analytics 3(4), 300–310 (2020)
Liu, Y., et al.: Forest pest identification based on a new dataset and convolutional neural network model with enhancement strategy. Comput. Electron. Agric. 192, 106625 (2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Madhavan, M.V., Thanh, D.N.H., Khamparia, A., Pande, S., Malik, R., Gupta, D.: Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques. Comput. Mater. Continua 66(3), 2939–2955 (2021)
Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollar, P.: Designing network design spaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
Ramesh, S., et al.: Plant disease detection using machine learning. In: 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), pp. 41–45 (2018). https://doi.org/10.1109/ICDI3C.2018.00017
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Rothe, P., Kshirsagar, R.: Automated extraction of digital images features of three kinds of cotton leaf diseases. In: 2014 International Conference on Electronics, Communication and Computational Engineering (ICECCE), pp. 67–71 (2014)
Shuhan, L., Ye, S.J.: Using an image segmentation and support vector machine method for identifying two locust species and instars. J. Integr. Agric. 19(5), 1301–1313 (2020)
Sinha, D., El-Sharkawy, M.: Thin MobileNet: an enhanced mobilenet architecture. In: 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0280–0285 (2019). https://doi.org/10.1109/UEMCON47517.2019.8993089
Tan, M., Le, Q.: EfficientNet: Rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 6105–6114. PMLR (2019). https://proceedings.mlr.press/v97/tan19a.html
Wang, R., Jiao, L., Xie, C., Chen, P., Du, J., Li, R.: S-RPN: sampling-balanced region proposal network for small crop pest detection. Comput. Electron. Agric. 187, 106290 (2021)
Xiao, Z., Yin, K., Geng, L., Wu, J., Zhang, F., Liu, Y.: Pest identification via hyperspectral image and deep learning. SIViP 16(4), 873–880 (2022)
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Liu, B., Zhang, J., Yuan, T., Huang, P., Feng, C., Li, M. (2024). SSK-Yolo: Global Feature-Driven Small Object Detection Network for Images. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_22
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