2025 Vol.8 Apr.N02

• Title: A computer vision-based method for accurate growth monitoring of tea plantations with air-ground integration
• Name: Wanxin Liang1，Hongyan Lu1*，Mei Tang2，Cui Huang3
• Company: 1Wuzhou Medical College，Wuzhou, ,543100,China 2Wuzhou Vocational College,,Wuzhou, ,543000,China; 3Guangxi University of Science and Technology，Liuzhou,,545000,China
• Abstract:

   In order to realise the accurate monitoring of the growth situation of the air-ground integrated tea garden, a monitoring method is proposed based on computer vision technology. Based on the stereo vision technology in computer vision technology, the plant imaging analysis of open space integrated tea garden is carried out, corresponding to the collection of multi-angle open space integrated tea garden plant target images; on the basis of the median filtering method of image processing, the plant target image features are extracted, and the obtained features are accurately monitored based on the method of locally maintained projection, so as to realise the accurate monitoring of the growth situation of open space integrated tea garden. The experimental results show that the average standard error of the monitoring results obtained by the design method is 0.10mm, and the maximum value of the monitoring standard error is only 0.13mm, which indicates that it can effectively realise the accurate monitoring of the growth situation of the tea plantation, and the practical application effect is good.

  
  

• Keyword: computer vision technology; air-ground integrated tea plantation; growth monitoring; stereo vision
• DOI: 10.12250/jpciams2025090402
• Citation form: Wanxin Liang,Hongyan Lu,Mei Tang,Cui Huang.A computer vision-based method for accurate growth monitoring of tea plantations with air-ground integration[J]. Computer Informatization and Mechanical System,2025,Vol.8,pp.

[1]SRI VARDHAN PERLA, MEGHA VAMSI KIRAN CHODA, YUVA TEJA ANIRUDH YELCHURU, et al. A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine[J]. Revue d'intelligence artificielle,2024,38(1):331-340.

[2]XUEQIAN LIU, JINGJING GUO, XUQI ZHENG, et al. Intelligent Plant Growth Monitoring System Based on LSTM Network[J]. IEEE sensors journal,2024,24(9):15073-15081.

[3]CHENG, ZHIKAI, GU, XIAOBO, DU, YADAN, et al. Multi-modal fusion and multi-task deep learning for monitoring the growth of film-mulched winter wheat[J]. Precision Agriculture,2024,25(4):1933-1957.

[4]BHAMIDIPATI, KISHORE, MUPPIDI, SATISH, REDDY, P. V. BHASKAR, et al. Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT[J]. Applied biochemistry and biotechnology, Part A. enzyme engineering and biotechnology,2024,196(4):2289-2317.