The Implementation of CNN on Website-based Rice Plant Disease Detection

Prasetyo, Herlambang Dwi; Triatmoko, Hendi; Nurdiansyah,; Isnainiyah, Ika Nurlaili

doi:10.1109/icimcis51567.2020.9354329

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…Herlambang Dwi Prasetyo et al [11] developed the web-based application using GoogLeNet architecture and Inception modules for rice plant disease detection to be used by end users. Chen et al [12] used the transfer learning concept by combining the DenseNet pretrained on ImageNet Dataset with the Inception module and showed good accuracy for the public dataset.…”

Section: Related Workmentioning

confidence: 99%

Generic Paddy Plant Disease Detector (GP2D2)

Prathima¹,

G²,

Sivachandar³

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too.

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Section: Related Workmentioning

confidence: 99%

Generic Paddy Plant Disease Detector (GP2D2)

Prathima¹,

G²,

Sivachandar³

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…InceptionV3 uses approximately 25 million parameters, but in this study, the number of parameters was significantly reduced to around 9.7 million. This significant reduction is an important contribution, especially for lightweight implementations, as it enhances model efficiency and mitigates overfitting with fewer classes [36].…”

Section: Enhanced Inceptionv3mentioning

confidence: 99%

Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation

Adityawan,

Farroq,

Santosa

et al. 2023

J. Comput. Theor. Appl.

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Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.

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“…In our study, we found that although other mainstream models such as AlexNet and VGG19 performed well in rice disease-detection tasks, numerous scholars have also employed these two networks to detect rice diseases. However, their accuracy rates largely plateaued around 92% [25,36,37]. On the other hand, several scholars utilized ResNet or networks with residual structures for rice disease detection, generally achieving higher accuracies, primarily in the vicinity of 95% [26,27,38,39].…”

Section: Limitations Of Other Mainstream Modelsmentioning

confidence: 99%

ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

et al. 2023

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Rice is a staple food for over half of the global population, but it faces significant yield losses: up to 52% due to leaf blast disease and brown spot diseases, respectively. This study aimed at proposing a hybrid architecture, namely ResViT-Rice, by taking advantage of both CNN and transformer for accurate detection of leaf blast and brown spot diseases. We employed ResNet as the backbone network to establish a detection model and introduced the encoder component from the transformer architecture. The convolutional block attention module was also integrated to ResViT-Rice to further enhance the feature-extraction ability. We processed 1648 training and 104 testing images for two diseases and the healthy class. To verify the effectiveness of the proposed ResViT-Rice, we conducted comparative evaluation with popular deep learning models. The experimental result suggested that ResViT-Rice achieved promising results in the rice disease-detection task, with the highest accuracy reaching 0.9904. The corresponding precision, recall, and F1-score were all over 0.96, with an AUC of up to 0.9987, and the corresponding loss rate was 0.0042. In conclusion, the proposed ResViT-Rice can better extract features of different rice diseases, thereby providing a more accurate and robust classification output.

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The Implementation of CNN on Website-based Rice Plant Disease Detection

Cited by 9 publications

References 2 publications

Generic Paddy Plant Disease Detector (GP2D2)

Generic Paddy Plant Disease Detector (GP2D2)

Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation

ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

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