A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery

Divyanth, L. G.; Ahmad, Aanis; Saraswat, Dharmendra

doi:10.1016/j.atech.2022.100108

Cited by 56 publications

(24 citation statements)

References 30 publications

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“…The authors of [14] proposed a two-stage segmentation technique based on deep learning and corn field data for crop disease quantification. In the first step of the model, modified Unet was utilised to segment leaves, followed by a DeepLabV3+ model for segmenting disease lesions.…”

Section: Related Workmentioning

confidence: 99%

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Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and Classification

Abinaya,

Kumar,

Alphonse

2023

IEEE Access

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Plant leaf diseases pose a significant threat to global food security and cause substantial economic losses. The objective of this study is to develop an effective approach for early detection and accurate identification of plant leaf diseases using computer vision techniques. The proposed method, Cascading Autoencoder with Attention Residual U-Net (CAAR-UNet), leverages deep learning to achieve precise segmentation and classification of plant leaf diseases. By cascading Symmetric Autoencoders with Attention Residual U-Net model and training on a custom dataset, it surpassed existing methods in identifying four disease classes. The model achieves remarkable accuracy, with a mean pixel accuracy of 95.26% and a weighted mean intersection over union of 0.7451, accurately capturing individual pixels and delineating disease class boundaries. This approach holds great potential in facilitating early plant disease detection and improving crop management practices. Its adoption can significantly impact food security worldwide, addressing a critical gap in the agricultural sector. The results highlight the effectiveness of the proposed strategy in plant disease management and open the door for further research in this field.

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Section: Related Workmentioning

confidence: 99%

“…For each class j (j = 1, 2, ..., c) and each image i (i = 1, 2, ..., n), the IoU score is presented as (14).…”

Section: ) Weighted Mean Intersection Overmentioning

confidence: 99%

Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and Classification

Abinaya,

Kumar,

Alphonse

2023

IEEE Access

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“…The potential of deep-learning algorithms has been demonstrated in almost all stages of agricultural activities, paving the way for efficient handling and non-destructive evaluation [1][2][3][4][5][6][7]. One of the agricultural domains that could benefit from these algorithms is weed management.…”

Section: Introductionmentioning

confidence: 99%

Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

et al. 2022

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Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to prepare very large image datasets by creating artificial images of maize (Zea mays) and four common weeds (i.e., Charlock, Fat Hen, Shepherd’s Purse, and small-flowered Cranesbill) through conditional Generative Adversarial Networks (cGANs). The fidelity of these synthetic images was tested through t-distributed stochastic neighbor embedding (t-SNE) visualization plots of real and artificial images of each class. The reliability of this method as a data augmentation technique was validated through classification results based on the transfer learning of a pre-defined convolutional neural network (CNN) architecture—the AlexNet; the feature extraction method came from the deepest pooling layer of the same network. Machine learning models based on a support vector machine (SVM) and linear discriminant analysis (LDA) were trained using these feature vectors. The F1 scores of the transfer learning model increased from 0.97 to 0.99, when additionally supported by an artificial dataset. Similarly, in the case of the feature extraction technique, the classification F1-scores increased from 0.93 to 0.96 for SVM and from 0.94 to 0.96 for the LDA model. The results show that image augmentation using generative adversarial networks (GANs) can improve the performance of crop/weed classification models with the added advantage of reduced time and manpower. Furthermore, it has demonstrated that generative networks could be a great tool for deep-learning applications in agriculture.

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“…Deep learning models have gained popularity in dealing with agricultural problems such as crop and weed species identification [ 17 ], plant disease detection [ 18 ], fruit counting and grading [ 19 ], food and grain quality monitoring [ 20 ], yield prediction [ 21 ], and crop stress phenotyping [ 22 , 23 ]. Phenomics techniques integrated with deep learning approaches can increase the throughput of plant phenotyping.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.)

Divyanth

Marzougui

González-Bernal

et al. 2022

Sensors

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Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important. Phenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable. One such approach is to couple imaging technologies with deep learning algorithms that are considered efficient for the assessment of disease resistance across a large number of plant genotypes. In this study, the resistance to ARR was evaluated through a CNN-based assessment of pea root images. The proposed model, DeepARRNet, was designed to classify the pea root images into three classes based on ARR severity scores, namely, resistant, intermediate, and susceptible classes. The dataset consisted of 1581 pea root images with a skewed distribution. Hence, three effective data-balancing techniques were identified to solve the prevalent problem of unbalanced datasets. Random oversampling with image transformations, generative adversarial network (GAN)-based image synthesis, and loss function with class-weighted ratio were implemented during the training process. The result indicated that the classification F1-score was 0.92 ± 0.03 when GAN-synthesized images were added, 0.91 ± 0.04 for random resampling, and 0.88 ± 0.05 when class-weighted loss function was implemented, which was higher than when an unbalanced dataset without these techniques were used (0.83 ± 0.03). The systematic approaches evaluated in this study can be applied to other image-based phenotyping datasets, which can aid the development of deep-learning models with improved performance.

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A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery

Cited by 56 publications

References 30 publications

Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and Classification

Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and Classification

Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.)

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