VGG-ICNN: A Lightweight CNN model for crop disease identification

Thakur, Poornima Singh; Sheorey, Tanuja; Ojha, Aparajita

doi:10.1007/s11042-022-13144-z

Cited by 92 publications

(41 citation statements)

References 47 publications

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“…In this section, many experiments of apple disease recognition are conducted to validate the proposed method DCCapsNet and compared with improved convolutional neural network (ICNN) ( Yan et al., 2020 ), VGG-ICNN ( Thakur et al., 2022 ), LAD-Net ( Zhu et al., 2022 ), and RegNet ( Li et al., 2022 ). The comparative experiments and results are analyzed and discussed.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Therefore, dilated convolution can be used for constructing a lightweight CNN model (Fang et al, 2019). Thakur et al (2022) introduced a lightweight CNN, namely, VGG-ICNN, for the identification of crop diseases using plant-leaf images. It consists of approximately 6 million parameters that are substantially fewer than most of the available high-performing deep learning models.…”

Section: Some Deep Learning Approaches Have Recently Been Introducedmentioning

confidence: 99%

“…Therefore, dilated convolution can be used for constructing a lightweight CNN model ( Fang et al., 2019 ). Thakur et al. (2022) introduced a lightweight CNN, namely, VGG-ICNN, for the identification of crop diseases using plant-leaf images.…”

Section: Introductionmentioning

confidence: 99%

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Dilated convolution capsule network for apple leaf disease identification

Wang

Zhang

2022

Front. Plant Sci.

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Accurate and rapid identification of apple leaf diseases is the basis for preventing and treating apple diseases. However, it is challenging to identify apple leaf diseases due to their various symptoms, different colors, irregular shapes, uneven sizes, and complex backgrounds. To reduce computational cost and improve training results, a dilated convolution capsule network (DCCapsNet) is constructed for apple leaf disease identification based on a capsule network (CapsNet) and two dilated Inception modules with different dilation rates. The network can obtain multi-scale deep-level features to improve the classification capability of the model. The dynamic routing algorithm is used between the front and back layers of CapsNet to make the model converge quickly. In DCCapsNet, dilated Inception instead of traditional convolution is used to increase the convolution receptive fields and extract multi-scale features from disease leaf images, and CapsNet is used to capture the classification features of changeable disease leaves and overcome the overfitting problem in the training network. Extensive experiment results on the apple disease leaf image dataset demonstrate that the proposed method can effectively identify apple diseases. The method can realize the rapid and accurate identification of apple leaf disease.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Some Deep Learning Approaches Have Recently Been Introducedmentioning

confidence: 99%

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Dilated convolution capsule network for apple leaf disease identification

Wang

Zhang

2022

Front. Plant Sci.

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“…Thakur and Sheorey et al [22] presents a model for the identification of plant disease with leaf imagery. Convolutional Neural Network named 'VGG-ICNN'.…”

Section: Related Workmentioning

confidence: 99%

Improvement Classification Approach in Tomato Leaf Disease using Modified Visual Geometry Group (VGG)-InceptionV3

Thomkaew¹,

Intakosum²

2022

IJACSA

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This paper presents a new method for optimizing tomato leaf disease classification using Modified Visual Geometry Group (VGG)-InceptionV3. Improved performance of VGG-16 model as a base model with InceptionV3 block reduced the number of convolution layers of VGG-16 from 16 to 10 layers, and added an InceptionV3 block that was improved by adding convolution layer from 3 to 4 layers to increase the accuracy of tomato leaf disease classification and reduce the number of parameters and computation time of the model. The experiments were performed on tomato leaves from the PlantVillage dataset of 10 classes, consisting of nine classes of diseased leaves and one class of healthy leaves. The results showed that the proposed method was able to reduce the number of parameters and computation time with and accuracy of tomato leaf disease classification was 99.27%. Additionally, the proposed approach was compared with state-of-the-art Convolutional Neural Network (CNN) models such as VGG16, InceptionV3, DenseNet121, MobileNetV2, and RestNet50. Comparative results showed that the proposed method had the highest accuracy in the tomato leaf disease classification and required a smaller number of parameters and computational time.

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“…Two spotted spider mites are a threat to 1000 plant species. Corynespora casiicola infects greenhouse and field tomato plants 6 …”

Section: Introductionmentioning

confidence: 99%

Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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Background Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. Methodology An enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. Results The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. Conclusion The presented model attained 99.2% accuracy compared to the cutting‐edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre‐trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

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VGG-ICNN: A Lightweight CNN model for crop disease identification

Cited by 92 publications

References 47 publications

Dilated convolution capsule network for apple leaf disease identification

Dilated convolution capsule network for apple leaf disease identification

Improvement Classification Approach in Tomato Leaf Disease using Modified Visual Geometry Group (VGG)-InceptionV3

Using a novel convolutional neural network for plant pests detection and disease classification

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