Advancing jasmine tea production: YOLOv7-based real-time jasmine flower detection.

Abstract

Background To produce jasmine tea of excellent quality, it is crucial to select jasmine flowers at their optimal growth stage during harvesting. However, achieving this goal remains a challenge due to environmental and manual factors. This study addresses this issue by classifying different jasmine flowers based on visual attributes using the YOLOv7 algorithm, one of the most advanced algorithms in convolutional neural networks. Results The mean average precision (mAP value) for detecting jasmine flowers using this model is 0.948, and the accuracy for five different degrees of openness of jasmine flowers, namely small buds, buds, half-open, full-open and wiltered, is 87.7%, 90.3%, 89%, 93.9% and 86.4%, respectively. Meanwhile, other ways of processing the images in the dataset, such as blurring and changing the brightness, also increased the credibility of the algorithm. Conclusion This study shows that it is feasible to use deep learning algorithms for distinguishing jasmine flowers at different growth stages. This study can provide a reference for jasmine production estimation and for the development of intelligent and precise flower-picking applications to reduce flower waste and production costs. © 2024 Society of Chemical Industry.

Plant phenotyping relevance

YOLOv7画像解析を用いてジャスミン花の開花・生育段階を分類する手法が研究の中心であり、花器官の状態という植物形質を直接推定している。

Code and data availability

The paper's jasmine flower image dataset (734 original photos, expanded to 10,840 with LabelImg annotations) and YOLOv7 analysis are described in detail, but no public repository, code deposit, or authors' URL is provided. The only availability statement directs readers to contact the corresponding author, so the paper

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