A Comparative Study between Convolution Neural Networks and Multi-Layer Perceptron Networks for Hand-written Digits Recognition
Plant diseases are a threat to the food supply as they reduce the yield, and reduce the quality of fruits and grains. Hence, early identification and classification of plant diseases are essential. This paper aims to classify mango plant leaves into healthy and diseased using convolutional neural ne...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2022
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| الوصول للمادة أونلاين: | https://dspace.auk.edu.kw/handle/11675/9606 https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijcvr |
| الوسوم: |
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| الملخص: | Plant diseases are a threat to the food supply as they reduce the yield, and reduce the quality of fruits and grains. Hence, early identification and classification of plant diseases are essential. This paper aims to classify mango plant leaves into healthy and diseased using convolutional neural networks (CNNs). The performance comparison of CNN architectures, AlexNet, VGG-16 and ResNet-50 for mango plant disease classification is provided. These models are trained using the Mendeley dataset, validation accuracies are found and compared with and without the use of transfer learning models. AlexNet (25 layers, 6.2 million parameters) produces a testing accuracy of 94.54% and consumes less training time. ResNet-50 (117 layers, 23 million parameters) and VGG-16 (16 layers, 138 million parameters) have given testing accuracies of 98.56% and 98.26% respectively. Therefore, based on the accuracies achieved and complexity, this paper recommends AlexNet followed by ResNet-50 and VGG-16 for plant leaf disease classification. |
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