[1] Yang J Y, Liu S X, Hu T L et al. Occurrence of major diseases and insect pests and pesticide use in apple leaves in China in 2009[J]. China Plant Protection, 30, 25-28, 17(2010).
[2] Tian Y Q. Several fruit diseases affecting the development of apple industry at home and abroad[J]. Yantai Fruits, 40-41(2014).
[3] Zhao Z H, Zhu X M, Liu W C. Challenges and opportunities for green prevention and control of medicinal plant diseases and pests in China[J]. China Plant Protection, 40, 103-106, 110(2020).
[4] He X Y, Zhao S L, Zhang Z et al. Development trend of the research and application of machine vision[J]. Machinery Design & Manufacture, 281-283, 287(2020).
[5] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).
[6] Varshney D, Babukhanwala B, Khan J et al. Machine learning techniques for plant disease detection[C], 1574-1581(2021).
[7] Wu L L, Zheng Z X, Qi L et al. Detection method of rice leaf blast based on image processing[J]. Journal of Agricultural Mechanization Research, 36, 32-35(2014).
[8] Padol P B, Yadav A A. SVM classifier based grape leaf disease detection[C], 175-179(2016).
[9] Zhang Y L, Yuan H, Zhang Q Q et al. Apple leaf disease identification method based on color feature and difference histogram[J]. Jiangsu Agricultural Sciences, 45, 171-174(2017).
[10] Radovanović D, Đukanovic S. Image-based plant disease detection: a comparison of deep learning and classical machine learning algorithms[C](2020).
[11] Hou X X, Gong Y H, Liu B Z et al. Learning based image transformation using convolutional neural networks[J]. IEEE Access, 6, 49779-49792(2018).
[12] Huang L S, Luo Y W, Yang X D et al. Crop disease recognition based on attention mechanism and multi-scale residual network[J]. Transactions of the Chinese Society for Agricultural Machinery, 52, 264-271(2021).
[13] He X, Li S Q, Liu B. Identification of grape leaf diseases based on multi-scale residual neural network[J]. Computer Engineering, 47, 285-291, 300(2021).
[14] Guo X Q, Fan T J, Shu X. Tomato leaf diseases recognition based on improved Multi-Scale AlexNet[J]. Transactions of the Chinese Society of Agricultural Engineering, 35, 162-169(2019).
[15] Zhang N, Wu H R, Han X et al. Tomato disease recognition scheme based on multi-scale and attention mechanism[J]. Acta Agriculturae Zhejiangensis, 33, 1329-1338(2021).
[16] Lu Z D, Zhang C D, Zhang J Q et al. Identification of apple leaf disease based on dual branch network[J]. Journal of Frontiers of Computer Science and Technology, 16, 917-926(2022).
[17] Wang J, Liu X H. Pathological recognition of apple leaves based on deeply separable convolution[J]. Computer Systems & Applications, 29, 190-195(2020).
[18] Zhang Z G, Yu P F, Li H Y et al. Image recognition of fine-grained wild bacteria based on multi-scale feature guidance[J]. Laser & Optoelectronics Progress, 59, 1210016(2022).
[19] Deng Z L, Li L. Chinese food recognition model based on improved residual network[J]. Laser & Optoelectronics Progress, 58, 0610019(2021).
[20] Li S Y, Liu Y H, Zhang R F. Fine-grained image classification based on multi-scale feature fusion[J]. Laser & Optoelectronics Progress, 57, 121002(2020).
[21] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[23] Wang M J. Research on remote recognition system of apple leaf diseases based on Andorid platform[D](2015).
[24] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 37, 448-456(2015).
[25] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[27] Szegedy C, Ioffe S, Vanhoucke V et al. Inception-v4, inception-Resnet and the impact of residual connections on learning[C], 4278-4284(2017).