Exploring capsule networks for disease classification in plants

Verma, Shradha; Chug, Anuradha; Singh, Amit Prakash

doi:10.1080/09720510.2020.1724628

Cited by 36 publications

(12 citation statements)

References 11 publications

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“…Values In Table 3, the first four studies in the table are based on the CNN algorithm. In the studies in [9,10] are based on capsule networks. As can be seen, these deep learning-based methods are quite successful.…”

Section: Resultsmentioning

confidence: 99%

“…0/. AlexNet inspired [6] 96.0 0.025 Pipeline CNNs [24] 95.15 0.018 VGG inspired [25] 95.22 0.105 Global pooling dilated CNN [26] 96.77 0.022 CapsNET [9] 95.08 0.652 Optimized CapsNET [10] 96.35 0.580 This Study-MCCNE 98.15 0.915…”

Section: Conflict Of Interestmentioning

confidence: 99%

“…With the proposed approach, 92.7% accuracy rate in the mini leaf data set and 94.3% accuracy rate in the sugarcane dataset were obtained. Verma et al [9] used capsule networks (CapsNet) to classify potato diseases and compared them with several popular pre-trained CNN models such as ResNet18, VGG16, and GoogLeNet to test the performance of their method. CapsNet has proven to achieve performance comparable to state-of-the-art CNN models with 91.83% accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Multi-channel capsule network ensemble for plant disease detection

Peker

2021

SN Appl. Sci.

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This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule networks, individually trained on images applied different preprocessing techniques and then combined together. In this way, the final ensemble can better detect plant diseases by making use of different attributes of the data. Our experiments carried out using a well-known data set and various state-of-the-art classification methods demonstrate that our classification approach can provide competitive advantages in terms of classification accuracy. Article Highlights An ensemble of capsule networks has been developed for the automated detection of plant diseases with high accuracies. Better accuracy has been achieved with ensemble learning compared to a single model With the proposed method, better results have been obtained compared to state-of-the-art classification methods in the literature

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Section: Resultsmentioning

confidence: 99%

Section: Conflict Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-channel capsule network ensemble for plant disease detection

Peker

2021

SN Appl. Sci.

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“…Other plant disease detection models in the literature [23][1] [24][25] [26] achieved good results, however, most of them are deep, complex, invariant, not robust, low performing, and lack flexibility. Additionally, they are invariant, cannot encode hue, texture, spatial orientation, and deformation.…”

Section: Introductionmentioning

confidence: 99%

Gabor Capsule Network for Plant Disease Detection

Mensah¹,

Weyori²,

Ayidzoe³

2020

IJACSA

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Crop diseases contribute significantly to food insecurity, malnutrition, and poverty in Africa where the majority of the population is into Agriculture. Manual plant disease recognition methods are widespread but limited, ineffective, costly, and time-consuming making the need to search for automatic and efficient methods of recognition more crucial. Machine learning and Convolutional Neural Networks have been applied in other jurisdictions in an attempt to solve these problems. They have achieved impressive results in this domain but tend to be 'data-hungry', invariant, and vulnerable to attacks that can easily lead to misclassifications. Capsule Networks, on the other hand, avoids the weaknesses of CNNs and has not been widely used in this area. This article, therefore, proposes the use of Gabor and Capsule network to recognize blurred, deformed, and unseen tomato and citrus disease images. Experimental results show that the proposed model can achieve a 98.13% test accuracy which is comparable to the performance of state-of-theart CNN models in the literature. Also, the proposed model outperformed two state-of-the-art deep learning models (which were implemented as baseline models) in terms of robustness, flexibility, fast converges, and having fewer parameters. This work can be extended to other crops and may well serve as a useful tool for the recognition of unseen plant diseases under bad weather and bad illumination conditions.

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“…utilized CapsNet algorithm to identify potato leaf diseases in PlantVillage dataset. They also experimented on various pre-trained CNN architectures including ResNet, VggNet, and GoogLeNet to compare the performance of CapsNet and highlighted the superiority of CapsNet over CNN architectures in accuracy [20]. Dong et al modified the CapsNet model by stacking three convolutional layers in addition the conventional CapsNet architecture for identification of peanut leaf diseases in their own dataset.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

Altan

2020

International Journal of Applied Mathematics Electronics and Computers

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Deep Learning (DL) is a high capable machine learning algorithm which composed the advanced image processing as feature learning and supervised learning with detailed models with many hidden layers and neurons. DL demonstrated its efficiency and robustness in many big data problems, computer vision, and more. Whereas it has an increasing popularity day by day, it has still some deficiencies to construe the relationship between learned feature maps and spatial information. Capsule network (CapsNET) is proposed to overcome the shortcoming by excluding the pooling layer from the architecture and transferring spatial information between layers by capsule. In this paper, CapsNET architecture was proposed to evaluate the performance of the model on classification of plant leaf diseases using simple reduced capsules on leaf images. Plant leaf diseases are common and prevalent diseases that disrupt harvesting and yielding for agriculture. CapsNET has capability of detailed analysis for even small stains that may lead seed dressing time and duration. The proposed CapsNET model aimed at assessing the applicability of various feature learning models and enhancing the learning capacity of the DL models for bell pepper plants. The healthy and diseased leaf images were fed into the CapsNET. The proposed CapsNET model reached high classification performance rates of 95.76%, 96.37%, and 97.49% for accuracy, sensitivity, and specificity, respectively.

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Exploring capsule networks for disease classification in plants

Cited by 36 publications

References 11 publications

Multi-channel capsule network ensemble for plant disease detection

Multi-channel capsule network ensemble for plant disease detection

Gabor Capsule Network for Plant Disease Detection

Performance Evaluation of Capsule Networks for Classification of Plant Leaf Diseases

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