Zhou Hanlin, Luo Jianlong, Ye Qiuping, Leng Wenjun, Qin Jingfeng, Lin Jing, Xie Xiaoyu, Sun Yilan, Huang Shiguo, Pang Jie
College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, China.
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China.
J Sci Food Agric. 2024 Dec;104(15):9297-9311. doi: 10.1002/jsfa.13752. Epub 2024 Jul 19.
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.
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.
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.
为了生产出品质优良的茉莉花茶，在采摘时选择处于最佳生长阶段的茉莉花至关重要。然而，由于环境和人工因素，实现这一目标仍然是一项挑战。本研究通过使用卷积神经网络中最先进的算法之一YOLOv7算法，根据视觉属性对不同的茉莉花进行分类，来解决这个问题。
使用该模型检测茉莉花的平均精度均值（mAP值）为0.948，对于茉莉花五种不同开放程度，即小花蕾、花蕾、半开、全开和枯萎，检测准确率分别为87.7%、90.3%、89%、93.9%和86.4%。同时，对数据集中图像进行的其他处理方式，如模糊和改变亮度，也提高了算法的可信度。
本研究表明，使用深度学习算法区分不同生长阶段的茉莉花是可行的。本研究可为茉莉花产量估计以及开发智能精准的采花应用提供参考，以减少花朵浪费和生产成本。© 2024化学工业协会。
