Abstract
Aiming at the problem of low accuracy and efficiency of artificial identification of peach diseases and pests, the RFBNet based on Kmeans++ is proposed to construct the image detector of peach diseases and pests respectively. The Kmeans++ algorithm is used to adjust the prior box size instead of manually setting the prior box size, which makes it match the size of diseases and pests better, so that small diseases and pests can get better detection results. Four kinds of disease images and five kinds of pest images were collected from peach orchard in Shandong Province to construct the data set of peach diseases and pests, and the data set was expanded by five kinds of data enhancement methods to enhance the generalization ability of the model. The experimental results show that using this algorithm to detect peach disease and pest images, the disease detection accuracy is 73.12%, and the pest detection accuracy is 94.02%, which are higher than the SSD and RFBNet.
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References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE, Los Alamitos, CA (2005)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of 12th International Conference on Pattern Recognition, pp. 582–585. IEEE, Los Alamitos, CA (1994)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of International Conference on Image Processing, pp. I-I. IEEE, Piscataway, NJ (2002)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE, Los Alamitos, CA (2014)
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. 39(6), 1137–1149 (2015)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: Object detection via region-based fully convolutional networks. Adv. Neural. Inf. Process. Syst. 29, 379–387 (2016)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE, Los Alamitos, CA (2016)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham, Switzerland (2016)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 1–11 (2016)
Fuentes, A., Yoon, S., Kim, S.C., Park, D.S.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)
Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., Menaka, R.: Attention embedded residual CNN for disease detection in tomato leaves. Appl. Soft Comput. 86, 105933 (2020)
Xinran, L., Shuqin, L., Bin, L.: Detection of apple leaf diseases based on improved Faster R_CNN. Comput. Eng., 1–7 (2020)
Ding, W., Taylor, G.: Automatic moth detection from trap images for pest management. Comput. Electron. Agric. 123, 17–28 (2016)
Shen, Y., Zhou, H., Li, J., Jian, F., Jayas, D.S.: Detection of stored-grain insects using deep learning. Comput. Electron. Agric. 145, 319–325 (2018)
Selvaraj, M.G., Vergara, A., Ruiz, H., Safari, N., Blomme, G.: AI-powered banana diseases and pest detection. Plant Methods 15(1), 92 (2019)
Wang, Q.J., Zhang, S.Y., Dong, S.F., Zhang, G.C., Yang, J., Li, R., Wang, H.Q.: Pest24: a large-scale very small object data set of agricultural pests for multi-target detection. Comput. Electron. Agric. 175, 105585 (2020)
Liu, S., Huang, D.: Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 385–400. Springer, Cham (2018)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp.1027–1035. SIAM, New Orleans (2007)
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This work was partially supported by First Class Discipline Funding of Shandong Agricultural University.
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Li, Q., Sun, W., Shi, A., Lei, C., Mu, S. (2022). Image Detection of Peach Diseases and Pests. In: Yao, J., Xiao, Y., You, P., Sun, G. (eds) The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021). Lecture Notes in Electrical Engineering, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-16-6963-7_46
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DOI: https://doi.org/10.1007/978-981-16-6963-7_46
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