Tea Leaf Disease Classification using Domain Adaptation Method

Wei Wu

doi:10.54097/fcis.v3i2.7187

Authors

Wei Wu

DOI:

https://doi.org/10.54097/fcis.v3i2.7187

Keywords:

Deep learning, Domain adaptation, Tea leaf disease classification

Abstract

Tea trees are extremely vulnerable to diseases and insect pests in the growth process, which seriously affects the yield and quality of tea leaves. This requires the identification and treatment of infected tea leaves in time. With the rapid development of artificial intelligence and computer vision, there is a new trend to identify the images by computer. However, there often exists environmental changes such as illumination intensity, sample angles and background in the leaf images collected in natural scenes, which results in the difference of data distribution. This makes the traditional deep learning method unable to solve the problem of cross-domain classification very well, thus seriously affecting the accuracy of classification. This study mainly focuses on the cross-domain task with unaligned data distribution. A domain-adaptive based method was proposed to realize the cross-domain classification of tea leaf diseases. The experimental results verify the effectiveness of the presented method and provide new thoughts for the cross-domain classification problem in agriculture.

Downloads

Download data is not yet available.

References

Chen J, Liu Q, Gao L. Visual tea leaf disease recognition using a convolutional neural network model[J]. Symmetry, 2019, 11(3): 343.

Gayathri S, Wise D C J W, Shamini P B, et al. Image analysis and detection of tea leaf disease using deep learning[C]//2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2020: 398-403.

Hu G, Yang X, Zhang Y, et al. Identification of tea leaf diseases by using an improved deep convolutional neural network[J]. Sustainable Computing: Informatics and Systems, 2019, 24: 100353.

Ganin Y, Ustinova E, Ajakan H, et al. Domain-adversarial training of neural networks[J]. The journal of machine learning research, 2016, 17(1): 2096-2030.

Chen Y, Li W, Sakaridis C, et al. Domain adaptive faster r-cnn for object detection in the wild[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3339-3348.

Hsu H K, Yao C H, Tsai Y H, et al. Progressive domain adaptation for object detection[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020: 749-757.

Tseng H Y, Lee H Y, Huang J B, et al. Cross-domain few-shot classification via learned feature-wise transformation[J]. arXiv preprint arXiv:2001.08735, 2020.

Marino S, Beauseroy P, Smolarz A. Unsupervised adversarial deep domain adaptation method for potato defects classification[J]. Computers and Electronics in Agriculture, 2020, 174: 105501.

Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]//International conference on machine learning. PMLR, 2015: 1180-1189.