DMT: A model detecting multispecies of tea buds in multi-seasons

Taojie Yu, Jianneng Chen, Zhiwei Chen, Yatao Li, Junhua Tong, Xiaoqiang Du

Abstract


In China, tea products made from fresh leaves characterized by one leaf with one bud (1L1B) are classified as “Famous Tea”, which has better taste and higher economic value, but suffers from a labor shortage. Aiming at picking automation, existing studies focus on visual detection of 1L1B, but algorithm validation is limited to a specific variety of tea sprouting in a certain harvest season at a certain location, which limits the engineering application of developed tea picking robots working in various natural tea fields. To address this gap, a deep learning model DMT (detecting multispecies of tea) based on YOLOX-S was proposed in this paper. The DMT network takes YOLOX-S as a baseline and adds ECA-Net to the CSP Darknet and FPN of YOLOX-S. The average precision (AP), precision, and recall of DMT are 94.23%, 93.39%, and 88.02%, respectively, for detecting 1L1B sprouting in spring; 93.92%, 93.56%, and 87.88%, respectively, for detecting 1L1Bsprouting in autumn. These experimental results are better than those of the five current object detection models. After fine-tuning the DMT network with another dataset composed of multiple tea varieties, the DMT network can detect 1L1B for different varieties of tea in multiple picking seasons. The results can promote the engineering application of picking automation of fresh tea leaves.
Keywords: tea buds, detection model, multispecies of tea, multi-season
DOI: 10.25165/j.ijabe.20241701.8021

Citation: Yu T J, Chen J N, Chen Z W, Li Y T, Tong J H, Du X Q. DMT: A model detecting multispecies of tea buds in multi-seasons. Int J Agric & Biol Eng, 2024; 17(1): 199-208.

Keywords


tea buds, detection model, multispecies of tea, multi-season

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References


Chacko S M, Thambi P T, Kuttan R, Nishigaki I. Beneficial effects of green tea: A literature review. Chinese Medicine, 2010; 5（1）: 13.

Chan S P, Yong P Z, Sun Y, Mahendran R, Wong J C M, Qiu C, et al. Associations of long-term tea consumption with depressive and anxiety symptoms in community-living elderly: Findings from the diet and healthy aging study. The Journal of Prevention of Alzheimer’s Disease, 2018; 5（1）: 21–25.

Zhu H K, Liu F, Ye Y, Chen L, Liu J Y, Gui A H, et al. Application of machine learning algorithms in quality assurance of fermentation process of black tea-based on electrical properties. Journal of Food Engineering, 2019; 263: 165–172.

Wang Y J, Li L Q, Liu Y, Cui Q Q, Ning J M, Zhang Z Z. Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision. Journal of Food Engineering, 2021; 304: 110599.

Han Y, Xiao H R, Qin G M, Song Z Y, Ding W, Mei S, et al. Developing situations of tea plucking machine. Engineering, 2014; 6（6）: 268–273.

Tang Y P, Han W M, Hu A G, Wang W Y. Design and experiment of intelligentized tea-plucking machine for human riding based on machine vision. Transactions of the CSAM, 2016; 47（7）: 15–20. （in Chinese）

Zhao R M, Bian X B, Chen J N, Dong C W, Wu C Y, Jia J M, et al. Development and test for distributed control prototype of the riding profiling tea harvester. Journal of Tea Science, 2022; 42（2）: 263–276. （in Chinese）

Lee J E, Lee B J, Hwang J A, Ko K S, Chung J O, Kim E H, et al. Metabolic dependence of green tea on plucking positions revisited: A metabolomic study. Journal of Agricultural and Food Chemistry, 2011; 59（19）: 10579–10585.

Zhang L, Zhang H D, Chen Y D, Dai S H, Li X M, Kenji I, et al. Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision. Int J Agric & Biol Eng, 2019; 12（1）: 6–9.

Chen Z W, He L Y, Ye Y, Chen J N, Sun L, Wu C Y, et al. Automatic sorting of fresh tea leaves using vision-based recognition method. Journal of Food Process Engineering, 2020; 43（9）: e13474.

Lu J, Huang Y, Lee K M. Feature-set characterization for target detection based on artificial color contrast and principal component analysis with robotic tealeaf harvesting applications. International Journal of Intelligent Robotics and Applications, 2021; 5: 494–509.

Zheng L, Zou L, Wu C Y, Jia J M, Chen J N. Method of famous tea bud identification and segmentation based on improved watershed algorithm. Computers and Electronics in Agriculture, 2021; 184: 106108.

Chen Y-T, Chen S-F. Localizing plucking points of tea leaves using deep convolutional neural networks. Computers and Electronics in Agriculture, 2020; 171: 105298.

Li Y T, He L Y, Jia J M, Lv J, Chen J N, Qiao X, et al. In-field tea shoot detection and 3d localization using an RGB-D camera. Computers and Electronics in Agriculture, 2021; 185: 106149.

Stein E, Shakarchi R. Fourier analysis an introduction. Princeton University Press. 2002; 309p.

Xu W K, Zhao L G, Li J, Shang S Q, Ding X P, Wang T W. Detection and classification of tea buds based on deep learning. Computers and Electronics in Agriculture, 2022; 192: 106547. doi:c10.1016/j.compag2021.106547.

Gill G S, Kumar A, Agarwal R. Monitoring and grading of tea by computer vision - A review. Journal of Food Engineering, 2011; 106（1）: 13–19.

Laddi A, Sharma S, Kumar A, Kapur P. Classification of tea grains based upon image texture feature analysis under different illumination conditions. Journal of Food Engineering, 2013; 115（2）: 226–231.

Wang Q L, Wu B G, Zhu P F, Li P H, Zuo W M, Hu Q H. ECA-Net: Efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）, Seattle: IEEE, 2020; pp.11531–11539. doi: 10.1109/CVPR42600.2020.01155.

Everingham M, Eslami S, Gool L V, Williams C, Winn J, Zisserman A. Assessing the significance of performance differences on the PASCAL VOC challenges via bootstrapping. Technical Note, 2013; pp.1–4.

Ge Z, Liu S T, Wang F, Li Z M, Sun J. YOLOX: Exceeding YOLO Series in 2021. arXiv e-Print archive, 2021. arXiv: 2107.08430.

Woo S, Park J, Lee J Y, Kweon I S. CBAM: Convolutional Block Attention Module. arXiv e-Print archive, 2018. arXiv: 1807.06521.

Hu J, Shen L, Albanie S, Sun G, Wu E H. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020; 42（8）: 2011–2023.

Liu W, Anguelov D< Erhan D, Szegedy C, Reed S, Fu C-Y, et al. SSD: Single Shot MultiBox Detector. In: Computer Vision - ECCV 2016, Springer, 2016; 9905: 21–37. doi: 10.1007/978-3-319-46448-0_2.

Ren S Q, He K M, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017; 39（6）: 1137–1149.

Bochkovskiy A, Wang C Y, Liao H. YOLOv4: Optimal speed and accuracy of object detection. arXiv e-print archive, 2020; arXiv: 2004.10934.

Yu X H, Gong Y Q, Jiang N, Ye Q X, Han Z J. Scale match for tiny person detection. In: 2020 IEEE Winter Conference on Application of Computer Vision （WACV）, Snowmass: IEEE, 2020; pp.1246–1254. doi: 10.1109/WACV45572.2020.9093394.




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