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Plant Disease Identification under Imbalanced Dataset using Hybrid Deep Learning Method
Changjian Zhou
Plant Disease Identification under Imbalanced Dataset using Hybrid Deep Learning Method - p19–29
Recent studies suggest that plant disease identification via computational approaches is vital for agricultural production. However, there is still a large gap that needs to be bridged while the training data is imbalanced when the number of samples in different categories of the dataset varies greatly. To solve this limitation, a hybrid deep learning method combining deep residual network, dense network, and deep convolution generative adversarial network (DCGAN) is proposed for plant disease identification in this work, which takes advantage of these three models. Including 34,501 original images with 33 categories are collected, where one category contains 4442 samples and another contains 74, causing the phenomenon of data imbalance. Importantly, the imbalanced dataset has a negative impact on training performance. To address this issue, the DCGAN is introduced for data augmentation to make up for the limit of training data in this research. In addition, the residual and dense network are combined as a novel deep learning method to improve prediction ability. Together, the original and generated images are integrated as a mixed dataset for training, and only the original images were utilized for testing. Experimental results indicated that the presented approach achieved 0.977 F1-score and 0.987 test accuracy, outperformed the existing state-of-the-art models. These findings indicate that the hybrid deep learning approach, through the ingenious integration of the strengths of three sub-networks, significantly enhances the generalization capability of the model. This methodology not only optimizes overall performance but also underscores the profound potential in tackling these complex problems. Furthermore, a smartphone-based point-to-point identification system was designed to provide convenience for users in practical application.
Horticulture
Plant Pathology
Plant Science
Plant Biotechnology
Plant Hybridization
Planting Field Trials
Plant Disease Identification under Imbalanced Dataset using Hybrid Deep Learning Method - p19–29
Recent studies suggest that plant disease identification via computational approaches is vital for agricultural production. However, there is still a large gap that needs to be bridged while the training data is imbalanced when the number of samples in different categories of the dataset varies greatly. To solve this limitation, a hybrid deep learning method combining deep residual network, dense network, and deep convolution generative adversarial network (DCGAN) is proposed for plant disease identification in this work, which takes advantage of these three models. Including 34,501 original images with 33 categories are collected, where one category contains 4442 samples and another contains 74, causing the phenomenon of data imbalance. Importantly, the imbalanced dataset has a negative impact on training performance. To address this issue, the DCGAN is introduced for data augmentation to make up for the limit of training data in this research. In addition, the residual and dense network are combined as a novel deep learning method to improve prediction ability. Together, the original and generated images are integrated as a mixed dataset for training, and only the original images were utilized for testing. Experimental results indicated that the presented approach achieved 0.977 F1-score and 0.987 test accuracy, outperformed the existing state-of-the-art models. These findings indicate that the hybrid deep learning approach, through the ingenious integration of the strengths of three sub-networks, significantly enhances the generalization capability of the model. This methodology not only optimizes overall performance but also underscores the profound potential in tackling these complex problems. Furthermore, a smartphone-based point-to-point identification system was designed to provide convenience for users in practical application.
Horticulture
Plant Pathology
Plant Science
Plant Biotechnology
Plant Hybridization
Planting Field Trials