Apple Surface Pesticide Residue Detection Method Based on Hyperspectral Imaging

Jia, Yue; He, Jinrong; Fu, Hongfei; Shao, Xi; Li, Zhaokui

doi:10.1007/978-3-030-02698-1_47

Cited by 7 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visible/near-infrared spectroscopy or hyperspectral imaging is a fast and non-destructive method to detect pesticide residues. Some studies have applied HSI to detect pesticide contaminants in foods [ 9 , 19 , 20 , 21 , 22 ], but the research object in those experiments was single and lacked mutual comparison between the objects. In our study, we chose three grapes to identify the difference between the varieties.…”

Section: Discussionmentioning

confidence: 99%

“…Sun et al used HSI technology (431–962 nm) to quantitatively identify the pesticide mixtures on lettuce leaves [ 18 ]. Jia et al detected apple surface pesticide residue based on HSI technology (865–1712 nm) [ 19 ]. Mohite et al used hyperspectral sensing (350–1052 nm) to detect pesticide (Cyantraniliprole) residue on grapes with no, single, and double doses [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Yan

Zhang

et al. 2022

Foods

View full text Add to dashboard Cite

Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Yan

Zhang

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…Jia [11] and others detected the pesticide residues on the surface of apple from 865.11nm to 1711.71nm. They tested two pesticides commonly used in apple, two pesticides (chlorpyrifos and carbendazim) and two pesticides mixed with apple control group without pesticide.…”

Section: Classification Of Hyperspectral Residual Pesticidesmentioning

confidence: 99%

Pesticide residue detection technology based on hyperspectral

Wang

2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2023)

View full text Add to dashboard Cite

“…At present, spectral analysis has become a hot spot in the identification of pesticide residues due to its excellent performance. The most widely used methods are nearinfrared spectroscopy (He et al, 2021b;, Raman spectroscopy (Zhu et al, 2021;Zhang et al, 2021a), terahertz time-domain spectroscopy (Chen et al, 2015), laser-induced breakdown spectroscopy (Martino et al, 2021;Wu et al, 2019), and hyperspectral imaging (Jia et al, 2018;Sun et al, 2018). Hyperspectral imaging technology combined with fluorescence emission technology has played an important role in the field of real-time online quality and safe non-destructive testing of agricultural products (Mahmudiono et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Identification of pesticide residues on black tea by fluorescence hyperspectral technology combined with machine learning

ZOU

Geng

et al. 2022

Food Sci. Technol

View full text Add to dashboard Cite

Black tea has a long history in China, but in export trade, pesticide residues often exceed the standard. To obtain a rapid, accurate, and non-destructive identification method of pesticide residues on black tea, the fluorescence hyperspectral data of dry black tea sprayed with distilled water and six pesticides were collected in this study. The spectra were preprocessed by multiplicative scatter correction (MSC) and standard normal variate (SNV). Then the uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive re-weighted sampling (CARS), UVE-SPA, and CARS-SPA were used to feature extraction. This study proposes a machine learning model composed of a one-dimensional convolutional neural network backbone (1D CNN backbone) and a random forest classifier (RF classifier) to identify pesticide residues on black tea, and the 1D CNN-RF model was compared with three other machine learning models (support vector machine, RF, and 1D CNN). The results show that MSC-CARS-SPA-1D CNN-RF is the best model for identifying pesticide residues on black tea in which the accuracy of the test set is 99.05%. Combined with fluorescence hyperspectral technology, the proposed 1D CNN-RF model has great potential in the non-destructive identification of pesticide residues on black tea.

show abstract

Apple Surface Pesticide Residue Detection Method Based on Hyperspectral Imaging

Cited by 7 publications

References 27 publications

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Pesticide residue detection technology based on hyperspectral

Identification of pesticide residues on black tea by fluorescence hyperspectral technology combined with machine learning

Contact Info

Product

Resources

About