背景:为了生产优质的茉莉花茶,在收获过程中,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个挑战。本研究通过使用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,卷积神经网络中最先进的算法之一。
结果:使用该模型检测茉莉花的平均精度(mAP值)为0.948,对于茉莉花的五种不同开放程度的精度,即小芽,芽,半开,全开而狂野,是87.7%,90.3%,89%,93.9%和86.4%,分别。同时,处理数据集中图像的其他方法,比如模糊和改变亮度,也提高了算法的可信度。
结论: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。该研究可为茉莉花产量估算和开发智能精准采花应用以减少花卉浪费和生产成本提供参考。©2024化学工业学会。
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.
CONCLUSIONS: 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.