利用数字化叶片识别植物物种——一个比较研究

Sumedh Patil, Baba Patra, Neha Goyal, Kapil O. Gupta
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引用次数: 8

摘要

植物在自然界和人类的福祉中起着至关重要的作用。它们对生态稳定做出了重大贡献,也是食品、药品和基本商业产品等我们需求的来源。由于大规模的森林砍伐、表层土壤侵蚀和栖息地破坏,现有植物物种的数量和类型都在稳步下降。因此,植物种植和物种鉴定与分类对于保护植物物种和促进农业发展至关重要,有助于更好地了解植物。然而,由于植物鉴定需要领域知识和经验,它们很难运用。然而,由于机器学习和深度学习的进步,这个问题得到了正确的解决。各种机器学习和深度学习算法,如支持向量机、人工神经网络、卷积神经网络、概率神经网络等,已经成功地在植物叶片图像上进行了实验,以接近正确的精度识别物种。本文试图对用于植物鉴定的各种方法进行比较分析。几个瑞典叶的实验证实了机器学习和基于CNN的分类模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recognizing Plant species using Digitized leaves- A comparative study
Plants play a crucial role in nature and the well-being of the population. They have a significant contribution towards ecological stability and are also sources of our needs like food, medicine, and essential commercial products. As a result of massive scale deforestation, topsoil erosion, and habitat destruction, both the number and type of plants' existing species are steadily declining. So, plantation and identification and classification of plant species are essential for preserving plant species and accelerated farm as it will help in the better understanding of plants. Nevertheless, they are difficult to exercise as plant identification needs domain knowledge and experience. However, due to advances in machine learning and deep learning, this problem is tackled correctly. Various machine Learning and Deep Learning algorithms like Support Vector Machine, Artificial Neural Network, Convolutional Neural Network, Probabilistic Neural Network have successfully experimented on plant leaf images to identify the species with near correct accuracy. This article attempts a comparative analysis of various approaches used for plant identification. Several experiments with Swedish leaves confirm the effectiveness of machine learning and CNN based classification model.
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