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Full-Text Articles in Food Biotechnology
A Review Of Near-Infrared Spectroscopy For Pesticide Residue Detection In Fruits And Vegetables, Mai Xinyun, Hu Fangyang, He Lingling, Niu Junle, Wei Lanjie
A Review Of Near-Infrared Spectroscopy For Pesticide Residue Detection In Fruits And Vegetables, Mai Xinyun, Hu Fangyang, He Lingling, Niu Junle, Wei Lanjie
Food and Machinery
Near -infrared spectroscopy (NIRS ) is valuable in pesticide residue detection for fruits and vegetables due to its nondestructive, rapid, and efficient nature. This review summarizes current research status in this field, compares the applicability of different spectral bands and imaging techniques, and analyzes challenges in single and mixed pesticide residue detection alongside enhancement strategies. Additionally, approaches to spectral preprocessing and feature wavelength selection are examined, with performance comparisons between chemometrics and deep learning models. Furthermore, the role of calibration transfer and transfer learning is discussed in improving model generalization across instruments and crop varieties. With continuous advancements in spectral …
A Non-Destructive Appearance Quality Detection Method For Primary-Processed Crayfish Based On Improved Yolov 11, Wang Xujia, Wang Shuqing, Hu Haoyu
A Non-Destructive Appearance Quality Detection Method For Primary-Processed Crayfish Based On Improved Yolov 11, Wang Xujia, Wang Shuqing, Hu Haoyu
Food and Machinery
[Objective] To achieve rapid and non -destructive online detection of the appearance quality of crayfish during primary processing and to improve detection accuracy and real -time performance in complex scenarios. [Methods] A dataset for crayfish appearance quality is constructed for complex scenarios on primary -processing production lines, and a crayfish appearance quality detection algorithm YOLO -CQ based on YOLOv 11n is proposed. DualConv is introduced to improve the C 3k2 module, enhancing the model's feature extraction capability while optimizing the allocation of computational resources. Deformable attention (DAT ) is integrated into the C 2PSA module, enabling the model to adaptively …
A Surface Defect Detection Model For Fused Images Of Fruits Based On Improved Resnet 18, Si Haiping, Zhao Wenrui, Zhao Yuyang, Li Tingting, Fernado Bacao, Li Yanling
A Surface Defect Detection Model For Fused Images Of Fruits Based On Improved Resnet 18, Si Haiping, Zhao Wenrui, Zhao Yuyang, Li Tingting, Fernado Bacao, Li Yanling
Food and Machinery
[Objective] To enhance the accuracy of detecting fruit surface defects caused by minor mechanical friction during transportation. [Methods] A surface defect detection model for fused images of fruits based on improved ResNet 18 is proposed. To address the challenge of multi-scale feature extraction, the model incorporates a MELA module designed to capture image details more comprehensively. Additionally, an energy function-based regularization loss function is introduced to collaboratively optimize the training process alongside the cross-entropy loss function. Furthermore, a transfer learning strategy is employed to mitigate overfitting. [Results] Experimental results demonstrated that the improved model exhibits exceptional performance in fruit surface …
Design Of Apple Damage Automatic Detection System Based On Machine Vision, Qin Yinchu, Li Tao, Li Xu, Wang Meiling, Tan Zhiying
Design Of Apple Damage Automatic Detection System Based On Machine Vision, Qin Yinchu, Li Tao, Li Xu, Wang Meiling, Tan Zhiying
Food and Machinery
[Objective] To meet the practical requirements for comprehensive grading based on the appearance quality and size of apples, and to address issues such as low efficiency of manual sorting, complex structure, and high cost of sorting equipment for Chinese apples. [Methods] A YOLOv5s-apple model was proposed. The transformer module and CBAM attention module were introduced into the backbone network, and the weighted Bidirectional feature pyramid network (Bi-FPN) was added to improve the neck network. Then, combined with HALCON software, a self-designed intelligent apple damage detection system was used to carry out damage sorting and size classification. [Results] The experimental results …
A Red Fuji Apple Appearance Grading Method Based On Improved Whale Optimization Algorithm And Cnn, Liu Sujiao, Lu Mingxing, Wang Chunfang, Zhao Zifeng, Liu Yi
A Red Fuji Apple Appearance Grading Method Based On Improved Whale Optimization Algorithm And Cnn, Liu Sujiao, Lu Mingxing, Wang Chunfang, Zhao Zifeng, Liu Yi
Food and Machinery
Objective: In order to improve the accuracy of machine vision technology in grading the appearance quality of red Fuji apples, a red Fuji apple appearance grading method based on improved whale optimization algorithm （WOA） and CNN is proposed. Methods: A red Fuji apple image database with different appearance quality levels was established, and the database images were preprocessed so as to improve the training effect and generalization ability of the model. The improved CNN-LSTM was designed as the weighted grey correlation method was used to compress the CNN convolution scale, in order to reduce redundant interference between features and improve …
Fruit Variety And Freshness Recognition Method Based On Yolo-Ffd, Yan Zi, Chen Liangyan, Liu Weihua, Lai Huaqing, Ye Sheng
Fruit Variety And Freshness Recognition Method Based On Yolo-Ffd, Yan Zi, Chen Liangyan, Liu Weihua, Lai Huaqing, Ye Sheng
Food and Machinery
Objective: In order to improve the situation that existing fruit recognition and classification methods rely on manual operation and complex equipment. Methods: A lightweight model YOLO-FFD (YOLO with fruit and freshen detection) was proposed, which based on the YOLOv5 framework. Firstly, LightweightC3 was designed as the basic unit of the backbone feature extraction network based on the depth separable convolution and GELU activation function, which reduced the number of model parameters and computation, and speeds up the convergence of the model. Secondly, EnhancedC3, a large kernel depth separable convolution module, was used to improve the neck of the original model, …
The Application Of Computer Vision Combining With Deep Leaning Techniques For Rapid Discrimination Of Adulterated Star Anise Powder, Chen Jinxing
Food and Machinery
Objective: This study aims to design a novel approach, utilizing computer vision combining with deep learning, for rapid determination the adulteration in star anise powder. Methods: Collected the original images of star anise powder with varying adulteration ratios. Employing preprocessing and data enhancement techniques, an image dataset was curated. Subsequently, a SqueezeNet model was constructed and compared with five machine learning models, including Support Vector Machine （SVM）, K-Nearest Neighbor Learning （KNN）, Random Forest （RF）, Gradient Boosting Tree （GBT）, and Multilayer Perceptron （MLP）. Results: The highest accuracy achieved by the five machine learning models was only 66.37%, while the accuracy of …
Research Progress Of Food Packaging Defect Detection Based On Machine Vision, Ge Minghui, Zhang Jun, Lu Huijuan
Research Progress Of Food Packaging Defect Detection Based On Machine Vision, Ge Minghui, Zhang Jun, Lu Huijuan
Food and Machinery
Food packaging can develop defects during the production process due to various factors. The types of packaging defects are numerous with complex background. Machine vision detection, which uses visual imaging and computer information processing to complete tasks such as identification, detection, and measurement of packaging, has faster execution speed and higher accuracy compared to traditional manual inspection. This can significantly improve the degree of production automation. This article analyzes the common defects in food packaging and their causes, introduces traditional machine vision detection algorithms, and explores the research application of deep learning algorithms in food packaging defect detection. It also …
Research On Moulded Peanut Recognition Technology Based On Deep Learning, Wang Weina, Xu Shiwei, Deng Qinbo, Li Bo
Research On Moulded Peanut Recognition Technology Based On Deep Learning, Wang Weina, Xu Shiwei, Deng Qinbo, Li Bo
Food and Machinery
Objective: To identify mouldy peanuts in a fast and non-destructive way and improve the identification efficiency. Methods: Collected hyperspectral peanut data using a spectrometer, identify moldy peanuts using deep learning technology, and established a Hypernet PRMF model, which was compared with Deeplab v3+, Segnet, Unet, and Hypernet as control models. Integrated the proposed peanut recognition index into hyperspectral images as data feature pre extraction. Simultaneously integrating the constructed multi feature fusion blocks into the control model to improve the recognition efficiency of moldy peanuts. Results: The average pixel accuracy of all models exceeded 87%. the Hypernet-PRMF model had the highest …
Fast Recognition Method For Betel Nut In Dense Environments Based On Improved Yolo, Dai Yun, Lu Ming, He Ting, Peng Cheng-Wu
Fast Recognition Method For Betel Nut In Dense Environments Based On Improved Yolo, Dai Yun, Lu Ming, He Ting, Peng Cheng-Wu
Food and Machinery
Objective: This paper aimed to improve the accuracy of identification of small individual betel nuts and the degree of automation of betel nut processing plant by combining with deep learning. Methods: In this study, a novel feature extraction network named Mob-darknet-52 was proposed to construct a method of betel nut location and recognition based on improved YOLO algorithm by using multi-scale detection size. Results: the test showed that the proposed method had a detection accuracy of 94.8%, an accuracy rate of 94.5%, a recall rate of 95.1%, and a detection time of 6.679 ms in betel nut classification. Conclusion: The …
Detection Method Of Defective Coffee Beans Based On Yolov5, Zhang Cheng-Yao, Zhang Yan-Cheng, Zhang Yu-Qian, Zhao Yu-Qing
Detection Method Of Defective Coffee Beans Based On Yolov5, Zhang Cheng-Yao, Zhang Yan-Cheng, Zhang Yu-Qian, Zhao Yu-Qing
Food and Machinery
Objective: To realize the defect detection of coffee beans. Methods: An improved YOLOv5s network was proposed to embed different attention mechanism modules and activation functions with YOLOv5s as the baseline network. Results: The mean accuracy of the CBAM module and the activation function Hardswish improved by 5.3% and 2.9%, respectively, compared with the baseline network. After 200 iterations of training, the model accuracy was 99.5%, the average accuracy was 97.6%, the recall was 0.98, the recognition rate was 64 amplitude/s, and the model size was 15 M. Conclusion: Compared with Faster RCNN, SSD, YOLOv3, YOLOv4 and YOLOv5s, the test algorithm …
Localization And Counting Of String Tomatoes Based On Improved Tiny-Yolov5l Algorithm, Zhao Jiu-Xiao, Zhang Xin, Shi Kai-Li, Li Jing-Jing, Li Zuo-Lin
Localization And Counting Of String Tomatoes Based On Improved Tiny-Yolov5l Algorithm, Zhao Jiu-Xiao, Zhang Xin, Shi Kai-Li, Li Jing-Jing, Li Zuo-Lin
Food and Machinery
Objective: This study is to improve the sorting efficiency of string tomatoes, and solve its false detection and false detection. Methods: First, collect the image data set of string tomatoes, expand the data and improve the generalization performance of the model through data enhancement, and 3×3 convolution is replaced by improved SVM-MHSA layer. By replacing softmax classification function in MHSA with SVM classification function which is more suitable for string tomatoes, the detection accuracy of string tomatoes is enhanced. Secondly, the remaining 3×3 convolution is replaced by deep separable convolution to reduce the number of parameters and improve …
Classification Of Peanut Quality Based On Coordinated Attention, Wang Chun-Long, Jiang Zhong-Ming, Bao An-Hong
Classification Of Peanut Quality Based On Coordinated Attention, Wang Chun-Long, Jiang Zhong-Ming, Bao An-Hong
Food and Machinery
Objective: This study focuses on solving the problems of large memory consumption, low recognition accuracy and slow recognition speed in the classification process of peanut quality. Methods: A method for classification of peanut quality based on deep learning and image processing was proposed. The Coordinate Attention module was firstly introduced to encode the obtained feature graph into a pair of direction-aware and position-sensitive attention graph, which improved the ability to obtain the information of the region of interest of the graph. Then, Gradient Centralization was used to improve the optimizer. By modifying the parameters of the last fire …
A Method Of Tobacco Leaf Grade Recognition Based On Convolutional Neural Network, Jiao Fang-Yuan, Shen Jin-Yuan, Hao Tong-Meng
A Method Of Tobacco Leaf Grade Recognition Based On Convolutional Neural Network, Jiao Fang-Yuan, Shen Jin-Yuan, Hao Tong-Meng
Food and Machinery
Objective:To solve the problem of low accuracy of tobacco grading. Methods:An improved tobacco leaf grading model based on convolutional neural network was proposed. According to the VGG16 network structure, the network model was built in a custom way. The traditional convolution was replaced by the hole convolution, which increased the image receptive field while avoiding. The loss of image features was changed, and the activation function was changed to Leaky_relu. The data distribution was corrected, and the hard saturation problem of the ReLU function was solved. 41 levels of tobacco leaf pictures were used for testing. Results:The …