End-to-End Deep Learning Model for Corn Leaf Disease Classification

Amin, Hassan; Darwish, Ashraf; Hassanien, Aboul Ella; Soliman, Mostafa

doi:10.1109/access.2022.3159678

Cited by 114 publications

(33 citation statements)

References 19 publications

(42 reference statements)

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“…More specifically, the proposed framework attained an average accuracy value of 99.98% which is higher than other comparative methods. The studies ( Liu et al., 2020 ; Zhang et al., 2021 ; Amin et al., 2022 ) used various deep CNN architectures and the PlantVillage maize disease dataset as transfer learning. Few of them employed the attention method in CNNs to enhance classification accuracy ( Chen et al., 2021 ; Zeng et al., 2022a ; Qian et al., 2022 ; Yin et al., 2022 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This approach showed the highest prediction accuracy of 97.41% using ResNet50; however, the performance is limited over the noisy samples. In ( Amin et al., 2022 ), the authors developed a model using the EfficientNetB0, and DenseNet121 network to compute deep keypoints for the categorization of maize leaf disease. They fused the extracted features to obtain a more descriptive representation before performing classification.…”

Section: Related Workmentioning

confidence: 99%

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Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Albahli

Masood

2022

Front. Plant Sci.

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Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the existing automated methods are computationally complex or perform well on images with a simple background. Whereas, the realistic field conditions include a lot of background noise that makes this task difficult. In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases. To further enhance the capacity of the feature representation, we introduced a spatial-channel attention mechanism to focus on affected locations and help the detection network accurately recognize multiple diseases. We trained the EANet model using focal loss to overcome class-imbalanced data issues and transfer learning to enhance network generalization. We evaluated the presented approach on the publically available datasets having samples captured under various challenging environmental conditions such as varying background, non-uniform light, and chrominance variances. Our approach showed an overall accuracy of 99.89% for the categorization of various maize crop diseases. The experimental and visual findings reveal that our model shows improved performance compared to conventional CNNs, and the attention mechanism properly accentuates the disease-relevant information by ignoring the background noise.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Albahli

Masood

2022

Front. Plant Sci.

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“…Author achieved an accuracy of 96.75% with DRNN. On a publicly available dataset for plant disease classification, [17] the author used CNN on plant leaf images to identify healthy and unhealthy leaf images. The findings included image segmentation, LBP, and grab cut approaches, among other image processing techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed model was tested on a small dataset of fewer than 100 photos. The author [17] used EfficientB0 and DenseNet121 to find out the disease in corn. The author combined the features of EfficientB0 and DenseNet121 to find out the complex feature of the infected corn leaf images.…”

Section: Related Workmentioning

confidence: 99%

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

2022

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Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.

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“…However, little details are provided about the experimental setup in terms of how randomness in splitting the data is managed and how to guard against lucky choices. Recently, Amin et al [ 23 ] fused features from two CNN models, EfficientNetB0, and DenseNet121, to produce a more representative feature map and hence superior performance. They experimented with multiple fusion techniques and compared the performance of their approach, which produced a superior accuracy of 98.56%, to that of the ResNet152 and Inceptionv3 models.…”

Section: Introductionmentioning

confidence: 99%

Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning

Fraiwan

Faouri

Khasawneh

2022

Plants

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Protecting agricultural crops is essential for preserving food sources. The health of the plants plays a major role in impacting the yield of the agricultural output and results in significant economic loss. This is especially important in small-scale and hobby farming products such as fruits. Grapes are an important and widely cultivated plant especially in the Mediterranean region, with over $189 billion global market value. They are consumed as fruits as well as in other manufactured forms (e.g., drinks and sweet food products). However, much like other plants, grapes are prone to a wide range of diseases that require the application of immediate remedies. Misidentifying these diseases can result in poor disease control and great losses (i.e., 5–80% crop loss). Existing computer-based solutions may suffer from low accuracy, may require high overhead, are poorly deployable, and are prone to changes in image quality. The work in this paper aimed at utilizing ubiquitous technology to help farmers combat plant diseases. Particularly, deep learning artificial intelligence image-based applications were used to classify three common grape diseases—black measles, black rot, and isariopsis leaf spot. In addition, a fourth healthy class was included. A dataset of 3639 grape leaf images (1383 black measles, 1180 black rot, 1076 isariopsis leaf spot, and 423 healthy) was used. These images were used to customize and retrain eleven convolutional network models to classify the four classes. Thorough performance evaluation revealed that it is possible to design pilot and commercial applications that have accuracies that satisfy field requirements. The models achieved consistently high performance values (>99.1%).

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End-to-End Deep Learning Model for Corn Leaf Disease Classification

Cited by 114 publications

References 19 publications

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Meta Deep Learn Leaf Disease Identification Model for Cotton Crop

Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning

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