Identification of Green Gram (Vigna radiata) Grains Infested by Callosobruchus maculatus Through X-ray Imaging and GAN-Based Image Augmentation

Divyanth, L. G.; Chelladurai, V.; Loganathan, M.; Jayas, Digvir S.; Soni, P.

doi:10.1007/s42853-022-00147-9

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…In this paper, we use the term foreign object (FO) in a general manner, referring to the presence of undesirable structures within a certain base product. Examples of such objects include insect infestation in grain 5 , bones in fish 6 and chicken 7 fillets, but also structural damages such as fractures in light-alloy casting 8 and porosity in welds 9 . On a single X-ray view, different object features overlap with each other and make image analysis complicated even for a human expert.…”

Section: Ct-based Data Generation For Foreign Object Detection On a S...mentioning

confidence: 99%

CT-based data generation for foreign object detection on a single X-ray projection

Andriiashen

Liere

Leeuwen

et al. 2023

Sci Rep

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Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy.

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Section: Ct-based Data Generation For Foreign Object Detection On a S...mentioning

confidence: 99%

CT-based data generation for foreign object detection on a single X-ray projection

Andriiashen

Liere

Leeuwen

et al. 2023

Sci Rep

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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%

“…GANs have been effectively applied to various tasks, such as human identification [30], organ segmentation [31], and emotion classification [32]. These models have also been used for machine-vision applications in agriculture, such as generating images of specific plants [33,34], plant disease recognition [35], grain quality analysis [4], and for synthesizing images of plant seedlings [36]. A few studies have also utilized GANs to assist in deep-learning-based operations in precision weed management (Table 1).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Self Cite

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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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Identification of Green Gram (Vigna radiata) Grains Infested by Callosobruchus maculatus Through X-ray Imaging and GAN-Based Image Augmentation

Cited by 11 publications

References 41 publications

CT-based data generation for foreign object detection on a single X-ray projection

CT-based data generation for foreign object detection on a single X-ray projection

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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