Identification of tea plant cultivars based on canopy images using deep learning methods

作者：Zhi Zhang, Mengying Yang, Qingmin Pan, Xiaotian Jin, Guanqun Wang, Yiqiu Zhao, Yongguang Hu · 发表于：Scientia Horticulturae · 年份：2024 · DOI：10.1016/j.scienta.2024.113908 · 被引用次数：27 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Remote Sensing and Land Use

• A canopy image-based dataset containing 18 cultivars for tea plant cultivar identification was constructed. • 20 tea plant cultivar identification models were developed based on deep learning methods. • The identification performance of the model for tea plant cultivar was improved by optimal training process. • The accurate and non-destructive identification of tea plant cultivars in the field conditions was achieved. Accurate and rapid identification of tea plant cultivars in field conditions contributes significantly to improve the refined and intelligent production in tea plantations. However, the high phenotypic similarity among different tea plant cultivars, coupled with the morphological variation of the same cultivar under different nutritional statuses, growth periods, and environmental conditions, poses a substantial challenge for cultivar identification. Deep learning can automatically extract deep features from images, capturing critical information about various classes. Consequently, this study employed deep learning methods to identify tea plant cultivars using canopy images and investigated the effects of different training strategies on the model training outcomes. For this purpose, canopy images of tea plant in natural environment were used as the research object in this study, and a dataset for the identification of tea plant cultivars containing 8000 canopy images of 16 cultivars was constructed. 20 tea plant cultivars identification models were develope...