A Comparative Analysis of Deep Learning Models Applied for Disease Classification in Bell Pepper

Kundu, Nidhi; Rani, Geeta; Dhaka, Vijaypal Singh

doi:10.1109/pdgc50313.2020.9315821

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…Kundu et al [ 53 ] experimented with different deep learning models, videlicet, VGG16, VGG19, ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121 on the publicly available dataset of the bell pepper plant. Based on the analysis of results, the authors claim that the ‘DenseNet’ model outperforms the above-stated models in predicting diseases in bell pepper.…”

Section: Comparative Analysismentioning

confidence: 99%

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A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

Self Cite

231

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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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Section: Comparative Analysismentioning

confidence: 99%

“…Based on the analysis of results, the authors claim that the ‘DenseNet’ model outperforms the above-stated models in predicting diseases in bell pepper. They also claimed that the model is less computation-intensive and can be adopted for real-time prediction [ 53 ].…”

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

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231

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“…Several models of ML and DL are found effective and precise in disease detection and classification [ 18 ]. However, as per the discussion given in [ 19 , 20 ], the convolutional neural network (CNN) outperforms the machine learning (ML) models due to their potential in automatic feature extraction. However, CNN models demand a huge and labeled dataset for training.…”

Section: Introductionmentioning

confidence: 99%

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Kundu

Rani

Dhaka

et al. 2021

Sensors

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130

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Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.

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“…Experimental results showed that the proposed method achieves substantial improvement over other state-of-the-art methods. Kundu et al (2020) experimented with eight different deep learning models on the public dataset of the bell pepper. Their experimental results showed that the DenseNet model outperforms several other models in identifying sweet pepper diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results showed that the proposed method achieves substantial improvement over other state-of-the-art methods. Kundu et al. (2020) experimented with eight different deep learning models on the public dataset of the bell pepper.…”

Section: Introductionmentioning

confidence: 99%

Cotton disease identification method based on pruning

et al. 2022

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Deep convolutional neural networks (DCNN) have shown promising performance in plant disease recognition. However, these networks cannot be deployed on resource-limited smart devices due to their vast parameters and computations. To address the issue of deployability when developing cotton disease identification applications for mobile/smart devices, we compress the disease recognition models employing the pruning algorithm. The algorithm uses the γ coefficient in the Batch Normalization layer to prune the channels to realize the compression of DCNN. To further improve the accuracy of the model, we suggest two strategies in combination with transfer learning: compression after transfer learning or transfer learning after compression. In our experiments, the source dataset is famous PlantVillage while the target dataset is the cotton disease image set which contains images collected from the Internet and taken from the fields. We select VGG16, ResNet164 and DenseNet40 as compressed models for comparison. The experimental results show that transfer learning after compression overall surpass its counterpart. When compression rate is set to 80% the accuracies of compressed version of VGG16, ResNet164 and DenseNet40 are 90.77%, 96.31% and 97.23%, respectively, and the parameters are only 0.30M, 0.43M and 0.26M, respectively. Among the compressed models, DenseNet40 has the highest accuracy and the smallest parameters. The best model (DenseNet40-80%-T) is pruned 75.70% of the parameters and cut off 65.52% of the computations, with the model size being only 2.2 MB. Compared with the version of compression after transfer learning, the accuracy of the model is improved by 0.74%. We further develop a cotton disease recognition APP on the Android platform based on the model and on the test phone, the average time to identify a single image is just 87ms.

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A Comparative Analysis of Deep Learning Models Applied for Disease Classification in Bell Pepper

Cited by 16 publications

References 15 publications

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Cotton disease identification method based on pruning

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