用自建数据集和深度学习模型，精准识别药用植物叶片。
Identification of Traditional Medicinal Plant Leaves Using an effective Deep Learning model and Self-Curated Dataset
- 设计6层卷积神经网络，结合池化与全连接层提升识别能力。
- 在三个数据集上准确率最高达99.7%，优于传统方法。
- 适合需要自动化植物鉴定的医药研究与采集场景。
药用植物是传统与现代药物生产的关键，尤其在阿育吠陀医学中尤为重要。由于部分植物外观相似，正确识别并采集药用植物需依赖专家经验。为解决这一问题，减少对人工专家的依赖，本文提出一种定制化的卷积神经网络（CNN）模型，包含6个卷积层、最大池化层和全连接层。该模型在三个数据集上进行了测试：印度药用叶图像数据集、MED117药用植物叶数据集，以及作者自建数据集。使用Adam、RMSprop和带动量的SGD等优化器，模型分别达到99.5%、98.4%和99.7%的准确率。
原文摘要 · Abstract (English)
Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a crucial step due to the visually similar nature of some plants. The extraction of these plants from nonmedicinal plants requires human expert intervention. To solve the issue of accurate plant identification and reduce the need for a human expert in the collection process; employing computer vision methods will be efficient and beneficial. In this paper, we have proposed a model that solves such issues. The proposed model is a custom convolutional neural network (CNN) architecture with 6 convolution layers, max-pooling layers, and dense layers. The model was tested on three different datasets named Indian Medicinal Leaves Image Dataset,MED117 Medicinal Plant Leaf Dataset, and the self-curated dataset by the authors. The proposed model achieved respective accuracies of 99.5%, 98.4%, and 99.7% using various optimizers including Adam, RMSprop, and SGD with momentum.
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