Publication: Ensemble methods with deep convolutional neural networks for plant leaf recognition
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2021
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en
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item.page.harrt.identifier.callno
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ICIC Express Letters
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15
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6
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553
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565
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Ensemble methods with deep convolutional neural networks for plant leaf recognition
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Abstract
Recognition of plant leaves and diseases from images is a challenging task in computer vision and machine learning. This is because various problems directly affect the performance of the system; such as the leaf structure; differences of the intra-class; similarity of shape between inter-class; perspective of the image; and even recording time. In this paper; we propose the ensemble convolutional neural network (CNN) method to tackle these issues and improve plant leaf recognition performance. We trained five CNN models: MobileNetV1; MobileNetV2; NASNetMobile; DenseNet121; and Xception; accordingly to discover the best CNN based model. Ensemble methods; unweighted average; weighted average; and unweighted majority vote methods were then applied to the CNN output probabilities of each model. We have evaluated these ensemble CNN methods on a mulberry leaf dataset and two leaf disease datasets: tomato and corn leaf disease. As a result; the individual CNN model shows that MobileNetV2 outperforms every CNN model with an accuracy of 91.19% on the mulberry leaf dataset. The Xception combined with data augmentation techniques (Height Shift+Vertical Flip+Fill Mode) obtains an accuracy of 91.77%. We achieved very high accuracy above 99% from the DenseNet121 and Xception models on the leaf disease datasets. For the ensemble CNNs method; we selected the based models according to the best CNN models and predicted the output of each CNN with the weighted average ensemble method. The results showed that 3-Ensemble CNNs (3-EnsCNNs) performed better on plant leaf disease datasets; while 5-EnsCNNs outperforms on the mulberry leaf dataset. Surprisingly; the data augmentation technique did not affect the ensemble CNNs on the mulberry leaf and corn leaf disease datasets. On the other hand; application of data augmentation was slightly better than without only on the tomato leaf disease dataset.