Keywords
Tea plant disease
VGG16 deep learning
Submitted : 2024-09-15
Accepted : 2024-09-30
Published : 2024-10-15
Abstract
Tea plants are susceptible to diseases during their growth. These diseases seriously affect the yield and quality of tea. The effective prevention and control of diseases requires accurate identification of diseases. With the development of artificial intelligence and computer vision, automatic recognition of plant diseases using image features has become feasible. As the support vector machine (SVM) is suitable for high dimension, high noise, and small sample learning, this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants. An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty (C-DCGAN-GP) was used to expand the segmentation of tea plant spots. Finally, the Visual Geometry Group 16 (VGG16) deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.
References
Zhang Y, Jiang M, Yu P, et al., 2018, Agricultural Pest Image Recognition Method Based on Multi-Feature Fusion and Sparse Representation. Scientia Agricultural Sinica, 51(11): 2084–2093.
Hu G, Zhang X, Liang D, et al., 2019, Recognition of Pine Disease in Remote Sensing Images Based on Weighted Support Vector Data Description. Transactions of the Chinese Society for Agricultural Machinery, 44(5): 258–263.
Xie ZQ, Zhang HM, Zhang SW, et al., 2015, Cucumber Disease Recognition Based on Color Feature and Attribute Reduction. Journal of Jiangsu Agricultural Sciences, 2015(03): 526–530.
Lian X, Cheng K, An S, et al., 2019, Fruit Image Classification Based on Deep Learning and Transfer Learning. Measure and Control Technology, 38(06): 15–18.
Chen G, Zhao S, Cao L, et al., 2019, Maize Plant Disease Recognition Based on Transfer Learning and Convolutional Neural Network. Smart Agriculture, 1(02): 34–44.
Hu WW, Zhang W, Liu LZ, 2018, Image Recognition Of Wheat Leaf Disease Based on Variance—SFFS. Journal of Hunan Agricultural University (Natural Science Edition), 44(02): 225–228.
Hu G, Yang X, Zhang Y, et al., 2019, Identification of Tea Leaf Diseases by Using an Improved Deep Convolutional Neural Network. Sustainable Computing: Informatics and Systems, V24: 100353.
Zhu R, 2019, Research on Disease Recognition of Highland Barley Based on Support Vector Machine. Heilongjiang Science, 10(18): 1–3.
Tian J, 2015, Research on Wheat Leaf Disease Recognition Method Based on Image Analysis, thesis, Northwest A & F University.
Zhang X, Qiao Y, Meng F, et al., 2018, Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks. IEEE Access, 6: 30370–30377.
Guo X, Fan T, Shu X, 2019, Image Recognition of Tomato Leaf Disease Based on Improved Multi-Scale AlexNet. Transactions of the Chinese Society of Agricultural Engineering, 35(13): 162–169.
Zhang S, Zhang S, Zhang C, et al., 2019, Cucumber Leaf Disease Identification with Global Pooling Dilated Convolutional Neural Network. Computers and Electronics in Agriculture, 162: 422–430.
Gao T, Song S, Zhang J, et al., 2021, Image Segmentation of Rice Leaf Lesions Based on Threshold Segmentation Algorithm. Journal of Jilin Provincial Institute of Education, 37(10): 183–186.
Yang L, 2023, A Review of Threshold-Based Image Segmentation Algorithms: Principle, Classification and Typical Algorithms. Journal of Shenyang Normal University (Natural Science Edition), 41(06): 526–529.