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首页> 外文会议>International Conference on Information Communication Technology and System >FCNN-LDA: A Faster Convolution Neural Network model for Leaf Disease identification on Apple's leaf dataset
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FCNN-LDA: A Faster Convolution Neural Network model for Leaf Disease identification on Apple's leaf dataset

机译：FCNN-LDA：一种用于苹果叶片数据集上叶片疾病识别的快速卷积神经网络模型

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摘要

Fruits are common items bought by every household. They are delicious to eat and rich in nourishment. However they may also adversely affect health if the fruits are from a diseased tree/plant. Moreover, Farmers may also loose lot of amount of profit if their plants get affected by some disease. In this article, the main objective/goal is to develop a convolution neural network based approach to identify the disease in apple fruit. The data for experiment has been taken from PlantVillage. In the proposed work, a convolution neural network model has been developed to identify the disease in apple and it consists of three convolution layer, three max pooling layer followed by two densely connected layers. This model was formed after testing with varying number of convolution layers from 2 to 6 and found that 3 layer was giving best accuracy. For the result comparison purpose, the traditional machine learning algorithms are also executed on the same dataset. Along with traditional machine learning approaches, the famous pre-trained CNN models i.e. VGG16 and InceptionV3 are also executed. The experiments results shows the efficacy of proposed algorithm over pre-trained models and traditional machine learning approach in terms of accuracy, computational time, specificity, F1 score and AUC-ROC curve. The proposed model achieves the state of the art accuracy of 99%. Moreover, the proposed model requires only 20% of the space as compared to pre-trained model with inference time less than 1 second as pre-trained models require minimum 30 second.
机译：水果是每个家庭购买的普通物品。它们美味可口，营养丰富。但是，如果果实来自患病的树木/植物，它们也可能对健康造成不利影响。此外，如果农民的植物受到某种疾病的影响，他们可能还会失去很多利润。在本文中，主要目标/目标是开发一种基于卷积神经网络的方法来识别苹果果实中的疾病。实验数据取自PlantVillage。在拟议的工作中，已经开发了一种卷积神经网络模型来识别苹果中的疾病，该模型由三个卷积层，三个最大池化层以及两个紧密相连的层组成。该模型是在测试了2到6个不同数量的卷积层后形成的，发现3层具有最佳的准确性。为了进行结果比较，传统的机器学习算法也对同一数据集执行。与传统的机器学习方法一起，还执行了著名的预训练的CNN模型，即VGG16和InceptionV3。实验结果从准确性，计算时间，特异性，F1分数和AUC-ROC曲线方面证明了所提算法优于预训练模型和传统机器学习方法的有效性。所提出的模型实现了99％的最新精度。此外，与预训练模型相比，所建议的模型仅需要20％的空间，推理时间少于1秒，因为预训练模型需要最少30秒。

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