Convolutional neural network for maize leaf disease image classification
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TLDR
Based on the testing results, the best classification was AlexNet and Support Vector Machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.Abstract:
This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.read more
Citations
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Journal ArticleDOI
Identification of Maize Leaf Diseases based on Convolutional Neural Network
TL;DR: The classification results on three types of maize leaf diseases show that the two-channel Convolutional Neural Network has a better performance than the single AlexNet model.
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Identification of maize leaf diseases by using the SKPSNet-50 convolutional neural network model
TL;DR: Zhang et al. as discussed by the authors proposed a SKPSNet-50 convolutional neural network model to realize the accurate identification of maize leaf diseases in natural scene images, which replaces the 3 × 3 convolution kernel in the backbone network with Select Kernel-Point-Swish-B (SKPS), which is an improved building block of the selected kernel (SK) unit, and replaces the ReLU activation function with the Swish_B activation function to improve the feature extraction capability.
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Plant Disease Detection using AI based VGG-16 Model
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Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome images
D. Menaka,S. Ganesh Vaidyanathan +1 more
TL;DR: The performance metrics like F1 score, precision, support, sensitivity, Negative predictive value, Jaccard score and confusion matrix used for the evaluation of the proposed model prove that the proffered architecture is more suitable for classification of human chromosome images when compared to the other methods proposed in the recent literature.
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Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
Harry D. Mafukidze,Godliver Owomugisha,Daniel Otim,Action Nechibvute,Cloud Nyamhere,F. Mazunga +5 more
TL;DR: This work proposes a unique system that achieves the same objective while excluding input from specialists for maize leaf disease quantification, and develops an adaptive thresholding technique that automatically extracts the regions of interest from the class activation maps without any prior knowledge.
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