Deep learning-based approach for identification of diseases of maize crop

Haque, Md. Ashraful; Marwaha, Sudeep; Deb, Chandan Kumar; Nigam, Sapna; Arora, Alka; Hooda, K. S.; Soujanya, P. Lakshmi; Aggarwal, S. K.; Lall, Brejesh; Kumar, Mukesh; Islam, S. N.; Panwar, Mohit; Kumar, Prabhat; Agrawal, Ruchi

doi:10.1038/s41598-022-10140-z

Cited by 74 publications

(51 citation statements)

References 42 publications

(55 reference statements)

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“…Additionally, researchers are encouraged to publish the results achieved by using only the brightness to determine its effect. Haque et al [21] compared a model that was trained on rotation, distortion, and flipping to a model that was trained on brightness. The authors reported that the brightness-augmented model achieved slightly better results.…”

Section: -Results and Discussionmentioning

confidence: 99%

“…Nazaré et al [20] reached a similar conclusion, suggesting that noisy images can degrade CNNs' performance and that the images' quality is crucial. Haque et al [21] trained an InceptionV3 model to classify maize crop leaves to detect healthy leaves. They noticed that the brightness in the dataset was not uniform because the dataset was taken with on-file and not in-labcontrolled settings.…”

Section: -Introductionmentioning

confidence: 99%

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Brightness as an Augmentation Technique for Image Classification

2022

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Augmentation techniques are crucial for accurately training convolution neural networks (CNNs). Therefore, these techniques have become the preprocessing methods. However, not every augmentation technique can be beneficial, especially those that change the image’s underlying structure, such as color augmentation techniques. In this study, the effect of eight brightness scales was investigated in the task of classifying a large histopathology dataset. Four state-of-the-art CNNs were used to assess each scale’s performance. The use of brightness was not beneficial in all the experiments. Among the different brightness scales, the [0.75–1.00] scale, which closely resembles the original brightness of the images, resulted in the best performance. The use of geometric augmentation yielded better performance than any brightness scale. Moreover, the results indicate that training the CNN without applying any augmentation techniques led to better results than considering brightness augmentation. Therefore, experimental results support the hypothesis that brightness augmentation techniques are not beneficial for image classification using deep-learning models and do not yield any performance gain. Furthermore, brightness augmentation techniques can significantly degrade the model’s performance when they are applied with extreme values. Doi: 10.28991/ESJ-2022-06-04-015 Full Text: PDF

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Section: -Results and Discussionmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

Brightness as an Augmentation Technique for Image Classification

2022

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“…Several DL-based methods have been widely adopted for the classification of maize leaf disease due to their improved feature extraction and representation capabilities. Haque et al (2022) developed an Inceptionv3-based architecture for the classification of healthy maize leaves from diseased ones. Initially, several augmentation strategies such as flipping, rotating, skew, and distortion was applied to enhance the diversity of input data.…”

Section: Related Workmentioning

confidence: 99%

“…The CNNs are extensively applied for the categorization of various plant leaf diseases ( Sethy et al., 2020 ; Ngugi et al., 2021 ; Albattah et al., 2022 ). In a large number of studies, researchers fine-tuned pre-built CNN models such as AlexNet ( Rangarajan et al., 2018 ), GoogleNet ( Mohanty et al., 2016 ), ResNet ( Subramanian et al., 2022 ), InceptionNet ( Haque et al., 2022 ), Efficientnet ( Liu et al., 2020 ) and DenseNet ( Waheed et al., 2020 ; Baldota et al., 2021 ) employing transfer learning for leaf disease identification. Some of the studies have suggested novel CNN architecture for plant disease identification ( Picon et al., 2019 ; Agarwal et al., 2020 ; Zhang et al., 2021 ; Xiang et al., 2021 ).…”

Section: Introductionmentioning

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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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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“…In species-specific models, multiple disease categories of a particular crop type are considered for classification. For example, individual models are proposed for the recognition of diseases belonging to crop types, rice 15 , potato 16 , and maize 17 . In 15 , the deep CNN model trained to identify ten disease types of the rice plant achieved 95.48% accuracy, which is much higher than the existing approaches.…”

Section: Introductionmentioning

confidence: 99%

Family-based Plant Disease Characterization using Deep Neural Networks

Janarthan

Thuseethan

Rajasegarar

et al. 2022

Preprint

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Over the years, researchers have applied various deep learning techniques to automatically recognise plant diseases from both raster and spectral images. The primary focus of the existing studies is developing individual species-specific or disease-specific models, where the former recognises diseases of single crop type and the latter recognises single diseases of single or multiple crop types. Building one global model to recognise diseases of multiple crops has also been widely explored, where a class is treated as a crop-disease combination. While training individual species-specific or disease-specific deep models is labour-intensive, embracing a vast number of crop species and inherent diseases present on this planet makes the model cumbersome. In order to address this problem, a more intuitive and feasible family-based plant disease characterisation approach with botanical reasoning is proposed in this study. This approach demonstrates the feasibility of six state-of-the-art deep neural networks through a set of extensive experiments incorporating six key strategies. The results on a newly built family-based plant disease dataset confirm that the proposed novel approach is convincing to be applied in a plant family-based disease recognition problem. Further, this study creates future opportunities for more intuitive plant disease data collection and benchmark classification model development.

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Deep learning-based approach for identification of diseases of maize crop

Cited by 74 publications

References 42 publications

Brightness as an Augmentation Technique for Image Classification

Brightness as an Augmentation Technique for Image Classification

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

Family-based Plant Disease Characterization using Deep Neural Networks

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