Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Seo, Yong Weon; Kim, Giyoung; Lim, Jongguk; Lee, Ahyeong; Kim, Balgeum; Jang, Jae-Kyung; Mo, Changyeun; Kim, Moon S.

doi:10.3390/s21092899

Cited by 13 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to further verify the effect of artificial aging on seed vigor, standard germination experiments were performed on aged rice seeds ( Seo et al., 2021 ). The seeds of the samples were placed on moist germination paper in the numbering order.…”

Section: Methodsmentioning

confidence: 99%

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning

Qi,

Huang,

Sun

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

Vigor is one of the important factors that affects rice yield and quality. Rapid and accurate detection of rice seed vigor is of great importance for rice production. In this study, near-infrared hyperspectral imaging technique and transfer learning were combined to detect rice seed vigor. Four varieties of artificial-aged rice seeds (Yongyou12, Yongyou1540, Suxiangjing100, and Longjingyou1212) were studied. Different convolutional neural network (CNN) models were built to detect the vigor of the rice seeds. Two transfer strategies, fine-tuning and MixStyle, were used to transfer knowledge among different rice varieties for vigor detection. The experimental results showed that the convolutional neural network model of Yongyou12 classified the vigor of Yongyou1540, Suxiangjing100, and Longjingyou1212 through MixStyle transfer knowledge, and the accuracy reached 90.00%, 80.33%, and 85.00% in validation sets, respectively, which was better or close to the initial modeling performances of each variety. MixStyle statistics are based on probabilistic mixed instance-level features of cross-source domain training samples. When training instances, new domains can be synthesized, which increases the domain diversity of the source domain, thereby improving the generalization ability of the trained model. This study would help rapid and accurate detection of a large varieties of crop seeds.

show abstract

Section: Methodsmentioning

confidence: 99%

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning

Qi,

Huang,

Sun

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…The detection accuracy of the method was 100% and 95% for the raw juice sample diluted to 1:20 and the sample diluted to 1:50 to 1:100, respectively. Seo et al (2021) used VNIR-HSI technology to detect organic residues on metal surfaces of food processing machinery. In this study, potato and spinach juices of different concentrations were dropped onto the surface of stainless steel, and the droplets were allowed to air dry at room temperature for 24 hours to form dry residue spots.…”

Section: Contamination Detection Of Processing Equipmentmentioning

confidence: 99%

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

Tang

Zhang

Mujumdar

2022

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

Fresh-cut fruits and vegetables are healthy and convenient ready-to-eat foods, and the final quality is related to the raw materials and each step of the cutting unit. It is necessary to integrate suitable intelligent detection technologies into the production chain so as to inspect each operation to ensure high product quality. In this paper, several imaging technologies that can be applied online to the processing of fresh-cut products are reviewed, including: multispectral/hyperspectral imaging (M/HSI), fluorescence imaging (FI), X-ray imaging (XRI), ultrasonic imaging, thermal imaging (TI), magnetic resonance imaging (MRI), terahertz imaging, and microwave imaging (MWI). The principles, advantages, and limitations of these imaging technologies are critically summarized. The potential applications of these technologies in online quality control and detection during the fresh-cut processing are comprehensively discussed, including quality of raw materials, contamination of cutting equipment, foreign bodies mixed in the processing, browning and microorganisms of the cutting surface, quality/shelf-life evaluation, and so on. Finally, the challenges and future application prospects of imaging technology in industrialization are presented.

show abstract

“…Many effective technologies have been adopted for food defect-detection purposes [4], including electronic noses [5], X-ray [6], ultrasound [7], thermal imaging [8,9], fluorescence spectroscopy [10], terahertz imaging [11], and spectroscopy [12,13]. Each of these methods has its merits and restrictions.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection in Food Using Multispectral and High-Definition Imaging Combined with a Newly Developed Deep Learning Model

Deng,

Liu,

et al. 2023

Processes

View full text Add to dashboard Cite

The automatic detection of defects (cortical fibers) in pickled mustard tubers (Chinese Zhacai) remains a challenge. Moreover, few papers have discussed detection based on the segmentation of the physical characteristics of this food. In this study, we designate cortical fibers in pickled mustard as the target class, while considering the background and the edible portion of pickled mustard as other classes. We attempt to realize an automatic defect-detection system to accurately and rapidly detect cortical fibers in pickled mustard based on multiple images combined with a UNet4+ segmentation model. A multispectral sensor (MS) covering nine wavebands with a resolution of 870 × 750 pixels and an imaging speed over two frames per second and a high-definition (HD), 4096 × 3000 pixel resolution imaging system were applied to obtain MS and HD images of 200 pickled mustard tuber samples. An improved imaging fusion method was applied to fuse the MS with HD images. After image fusion and other preprocessing methods, each image contained a target; 150 images were randomly selected as the training data and 50 images as the test data. Furthermore, a segmentation model called UNet4+ was developed to detect the cortical fibers in the pickled mustard tubers. Finally, the UNet4+ model was tested on three types of datasets (MS, HD, and fusion images), and the detection results were compared based on Recall, Precision, and Dice values. Our study indicates that the model can successfully detect cortical fibers within about a 30 ± 3 ms timeframe for each type of image. Among the three types of images, the fusion images achieved the highest mean average Dice value of 73.91% for the cortical fibers. At the same time, we compared the UNet4+ model with the UNet++ and UNet3+ models using the same fusion data; the results show that our model achieved better prediction performance for the Dice values, i.e., 9.72% and 27.41% higher than those of the UNet++ and UNet3+ models, respectively.

show abstract

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Cited by 13 publications

References 33 publications

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning

Intelligent detection for fresh‐cut fruit and vegetable processing: Imaging technology

Defect Detection in Food Using Multispectral and High-Definition Imaging Combined with a Newly Developed Deep Learning Model

Contact Info

Product

Resources

About