Identification of rice diseases using deep convolutional neural networks

Lü, Yang; Yi, Shujuan; Zeng, Nianyin; Liu, Yurong; Zhang, Yong

doi:10.1016/j.neucom.2017.06.023

Cited by 774 publications

(338 citation statements)

References 17 publications

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“…In [16], Lu et al proposed a novel identification approach for rice diseases based on deep convolutional neural networks. Using a dataset of 500 natural images of diseased and healthy rice leaves and stems, CNNs were trained to identify 10 common rice diseases.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, an image database containing 10,888 images for training and 2801 images for testing was created. Chosen from experience, the size of the images was compressed from 2456 × 2058 to 256 × 256, which is able to be divided by 2 and reduces the training time [16].…”

Section: Building the Deep Convolutional Neural Networkmentioning

confidence: 99%

“…where λ means the λth layer, k ij represents the convolutional kernel, b j is the bias, and M j is a set of input feature maps [16].…”

Section: Convolution Layermentioning

confidence: 99%

“…Inspired by the breakthrough of the convolutional neutral network in image-based recognition, the use of convolutional neural networks to identify early disease images has become a new research hotspot in agricultural informatization. In [13][14][15][16][17][18][19][20], convolutional neural networks (CNNs) are widely studied and used in the field of crop disease recognition. These studies show that convolutional neural networks have not only reduced the demand of image preprocessing, but also improved the recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.

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Section: Related Workmentioning

confidence: 99%

Section: Building the Deep Convolutional Neural Networkmentioning

confidence: 99%

“…where λ means the λth layer, k ij represents the convolutional kernel, b j is the bias, and M j is a set of input feature maps [16].…”

Section: Convolution Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

et al. 2017

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“…CNNs are extensively used for solving image recognition tasks in computer vision. CNN architectures for tomato plant disease recognition used in (Sladojevic et al, 2016;Lu et al, 2017;Brahimi et al, 2017) usually either contain few convolution layers because of the small datasets or many convolution layers with large datasets. We use a simple baseline Resnet50 model for various experiments to analyze the effectiveness of classical and synthetic augmentation techniques using different instances of our tomato plant disease dataset.…”

Section: Cnn For Plant Disease Recognitionmentioning

confidence: 99%

Unsupervised image translation using adversarial networks for improved plant disease recognition

Nazki

Yoon

Fuentes

et al. 2020

Computers and Electronics in Agriculture

142

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Acquisition of data in task-specific applications of machine learning like plant disease recognition is a costly endeavor owing to the requirements of professional human diligence and time constraints. In this paper, we present a simple pipeline that uses GANs in an unsupervised image translation environment to improve learning with respect to the data distribution in a plant disease dataset, reducing the partiality introduced by acute class imbalance and hence shifting the classification decision boundary towards better performance. The empirical analysis of our method is demonstrated on a limited dataset of 2789 tomato plant disease images, highly corrupted with an imbalance in the 9 disease categories. First, we extend the state of the art for the GAN-based image-to-image translation method by enhancing the perceptual quality of the generated images and preserving the semantics. We introduce AR-GAN, where in addition to the adversarial loss, our synthetic image generator optimizes on Activation Reconstruction loss (ARL) function that optimizes feature activations against the natural image. We present visually more compelling synthetic images in comparison to most prominent existing models and evaluate the performance of our GAN framework in terms of various datasets and metrics. Second, we evaluate the performance of a baseline convolutional neural network classifier for improved recognition using the resulting synthetic samples to augment our training set and compare it with the classical data augmentation scheme. We observe a significant improvement in classification accuracy (+5.2%) using generated synthetic samples as compared to (+0.8%) increase using classic augmentation in an equal class distribution environment.

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Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

Daniya

Srinivasan

2022

Concurrency and Computation

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Summary The earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization‐based deep learning (SSSO‐based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak‐based UNet (BLSNet), multilayer maxout, resistance spot welding‐based deep recurrent neural network (RSW‐based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

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Identification of rice diseases using deep convolutional neural networks

Cited by 774 publications

References 17 publications

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

Unsupervised image translation using adversarial networks for improved plant disease recognition

Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

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