Design and experiment of online cottonseed quality sorting device

Wang, Qiaohua; Yu, Chengdong; Zhang, Hongzhou; Chen, Yuanzhe; Liu, C. T.

doi:10.1016/j.compag.2023.107870

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Regarding appearance detection, used aircoupled ultrasound with sound-to-image encoding for microcrack detection in cottonseeds, achieving a 90.7% accuracy. Wang et al (2023) applied machine vision technology with the YOLOV5 framework to detect damaged and mold-infested cottonseeds with over 99% accuracy. Du et al (2023) harnessed machine vision with the ResNet50 architecture for damaged cottonseed identification, reaching a 97.23% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Integrating spectral and image information for prediction of cottonseed vitality

Li,

Zhou,

Zhang

2023

Front. Plant Sci.

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Cotton plays a significant role in people’s lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality.

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Section: Introductionmentioning

confidence: 99%

Integrating spectral and image information for prediction of cottonseed vitality

Li,

Zhou,

Zhang

2023

Front. Plant Sci.

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Cotton-YOLO: Improved YOLOV7 for rapid detection of foreign fibers in seed cotton

Li,

Ma,

et al. 2024

Computers and Electronics in Agriculture

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Nondestructive testing of runny salted egg yolk based on improved ConvNeXt‐T

Chen,

Chang,

Chen

et al. 2024

Journal of Food Science

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Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt‐T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location‐aware circular convolution (ParC) was introduced, utilizing a ParC‐block to replace a portion of the original ConvNeXt‐T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC‐ConvNeXt‐SMU‐T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks.Practical ApplicationThis study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities.

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Design and experiment of online cottonseed quality sorting device

Cited by 5 publications

References 7 publications

Integrating spectral and image information for prediction of cottonseed vitality

Integrating spectral and image information for prediction of cottonseed vitality

Cotton-YOLO: Improved YOLOV7 for rapid detection of foreign fibers in seed cotton

Nondestructive testing of runny salted egg yolk based on improved ConvNeXt‐T

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