Classification of Plant Leaves of Western Ghats using Deep Learning

Sachin S. Bhat, Preema Dsouza, K. Sharanyalaxmi, Shreeraksha, Tejasvini, A. Ananth
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Abstract

Countless numbers of plants are available in this world. Identifying each and every plant and then classifying them has become one of the important and difficult tasks.Various parts of plants such as flowers, seeds, leaves can be used for identification, but recognizing leaves is the simplest and most effective method. Deep learning technique brings out effective way of leaf recognition system. Here we have used customised Convolutional Neural Network model to recognize the leaves specially growing in western ghats. A separate dataset has been created by collecting more than 50000 leaf samples of 48 different types of plants. The relevant information about the set of plants are collected from the botanists. Various architectures of CNN such as InceptionV3, MobileNet, VGG16, DensNet are used to evaluate the results. Model gives a satisfactory accuracy of 93.79% on 48 classes.
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基于深度学习的西高止山脉植物叶片分类
这个世界上有无数的植物。识别每一种植物并对其进行分类已成为重要而困难的工作之一。植物的各个部位如花、种子、叶子都可以用来鉴别,但辨认叶子是最简单、最有效的方法。深度学习技术为树叶识别系统提供了有效的方法。在这里,我们使用定制的卷积神经网络模型来识别特别生长在西部高山的叶子。通过收集48种不同类型植物的5万多个叶子样本,创建了一个单独的数据集。这组植物的有关资料是从植物学家那里收集来的。使用了CNN的各种架构,如InceptionV3、MobileNet、VGG16、DensNet来评估结果。模型在48个分类上的准确率为93.79%,令人满意。
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