Cocoon quality assessment system using vibration impact acoustic emission processing

Prasobhkumar, P.P.; Francis, C.R.; Gorthi, Sai Siva

doi:10.1016/j.eaef.2019.11.008

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…In recent years, the widespread adoption of artificial intelligence in agriculture has led to the exploration and implementation of machine vision technology for cocoon detection [5]. Prasobhkumar et al [6,7] presented a novel cocoon quality assessment system consisting of a conditioned illumination unit, an image acquisition unit, and a processing unit. The camera first acquired the images of cocoons, and then quantitative statistics on cocoon size, shape, and color were performed using morphological operations and ellipse fitting.…”

Section: Introductionmentioning

confidence: 99%

“…In the classification of mountage cocoons, Liu et al [11,12] proposed a waste cocoon detection method based on Fuzzy C-means clustering (FCM) and HSV color model. Firstly, FCM segmentation was applied to the original image of the mountage cocoons to Prasobhkumar et al [6,7] presented a novel cocoon quality assessment system consisting of a conditioned illumination unit, an image acquisition unit, and a processing unit. The camera first acquired the images of cocoons, and then quantitative statistics on cocoon size, shape, and color were performed using morphological operations and ellipse fitting.…”

Section: Introductionmentioning

confidence: 99%

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Sorting of Mountage Cocoons Based on MobileSAM and Target Detection

Liu,

Cui,

Wei

et al. 2024

Agriculture

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The classification of silkworm cocoons is essential prior to silk reeling and serves as a key step in improving the quality of raw silk. At present, cocoon classification mainly relies on manual sorting, which is labor-intensive and inefficient. In this paper, a cocoon detection algorithm S-YOLOv8_c based on the cooperation of MobileSAM and YOLOv8 for the mountage cocoons was proposed. The MobileSAM with a designed area thresholding algorithm was used for the semantic segmentation of mountage cocoon images, which could mitigate the effect of complex backgrounds and maximize the discriminability of cocoon features. Subsequently, the BiFPN was added to the neck of YOLOv8 to improve the multiscale feature fusion capability. The loss function was replaced with the WIoU, and a dynamic non-monotonic focusing mechanism was introduced to improve the generalization ability. In addition, the GAM was incorporated into the head to focus on detailed cocoon information. Finally, the S-YOLOv8_c achieved a good detection accuracy on the test set, with a mAP of 95.8%. Furthermore, to experimentally validate the sorting ability, we deployed the proposed model onto the self-developed Cartesian coordinate automatic cocoon harvester, which indicated that it would effectively meet the requirements of accurate and efficient cocoon sorting.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sorting of Mountage Cocoons Based on MobileSAM and Target Detection

Liu,

Cui,

Wei

et al. 2024

Agriculture

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“…[31]. Current methods see workers at the reeling center shake the cocoons one by one or sort them under sunlight because dead cocoons usually make no sound when shaken and appear black when exposed to sunlight [30] [32]. Development of technology that allows for the automatic identification of dead cocoons is therefore necessary for the automation of the sericulture industry.…”

Section: Introductionmentioning

confidence: 99%

Classification of Dead Cocoons Using Convolutional Neural Networks and Machine Learning Methods

Lee,

Kim,

Hong

et al. 2023

IEEE Access

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Image recognition methods classify or categorize objects by extracting significant properties from digital images of the objects and are used in the field of agriculture for quality determination. With the development of artificial intelligence technology, deep-learning techniques and tools such as convolutional neural networks (CNNs) have been used in image recognition. Existing discrimination studies have tended to extract features from images and classify them using multivariate analysis; however, deep learning algorithms have the self-learning ability to extract the feature points themselves for each neural layer. In this study, we developed models for discriminating dead cocoons using various discriminant analysis methods, including deep learning options, to establish an automation technology for the sericulture industry. A 100 W halogen light source was used for direct irradiation onto the cocoons, and a camera was positioned at the bottom of the cocoons (of which 43.9% were dead) to obtain RGB images. We conducted discrimination analyses based on the color space using four discrimination algorithms, namely, k-nearest neighbor, support vector machine, linear discriminant analysis, and partial least squares-discriminant analysis, within deep learning models (a proposed lightweight CNN model, VGG16, ResNet50, EfficientNetB0, MobileNet, ShuffleNet, GhostNet, and ConvNext). The proposed lightweight CNN model, which consisted of six convolutional layers and two fully connected layers, showed the highest discrimination accuracy (97.66%) in the Lab color space. It was thus confirmed that it is possible to automate the discrimination of dead cocoons using digital images and deep learning techniques.

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Cocoon quality assessment system using vibration impact acoustic emission processing

Cited by 2 publications

References 6 publications

Sorting of Mountage Cocoons Based on MobileSAM and Target Detection

Sorting of Mountage Cocoons Based on MobileSAM and Target Detection

Classification of Dead Cocoons Using Convolutional Neural Networks and Machine Learning Methods

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