Non-destructive detection and recognition of pesticide residues on garlic chive (Allium tuberosum) leaves based on short wave infrared hyperspectral imaging and one-dimensional convolutional neural network

He, Weiwen; He, Han; Wang, Fanglin; Wang, Shuyue; Lyu, Rulin

doi:10.1007/s11694-021-01012-7

Cited by 34 publications

(21 citation statements)

References 38 publications

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“…In 2021, He et al proposed a 1D CNN machine learning model to recognize pesticide residues on garlic chive leaves, which used the hyperspectral imaging system to collect the short-wave infrared hyperspectral images of garlic chive leaves sprayed with distilled water or pesticides, and the 1D CNN model was compared with other models. Finally, the accuracy rate of the 1D CNN model reached 98.5% (He et al, 2021b). However, due to the lack of input data, the 1D CNN model may have over-fitting, which is not explained.…”

Section: Introductionmentioning

confidence: 95%

“…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%

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

ZOU

Geng

et al. 2022

Food Sci. Technol

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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.

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Section: Introductionmentioning

confidence: 95%

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

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“…However, all the above studies detected a single pesticide. Considering the prevalence of using multiple pesticides in agriculture, He et al [59] proposed a deep learning model based on 1D-CNN to identify pesticide residues on leek leaves and predicted mixed pesticide residue samples based on this model with reasonable accuracy. The impact of heavy metals on food should not be underestimated either, and heavy metal pollution in the soil [83] is becoming increasingly severe due to the exploitation of mineral resources.…”

Section: Quantitative and Aualitative Analysis Of Chemical Compositionmentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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“…The residual neural network (ResNet) was shown to have a good effect on three-level residue discrimination in grapes [ 21 ]. Moreover, the one-dimensional convolutional neural network (1D-CNN) achieved the identification of pesticide residues on garlic chive leaves (λ-cyhalothrin, trichlorfon, phoxim, mixtures of trichlorfon and phoxim) [ 22 ], and also worked well on Hami melon (chlorothalonil, imidacloprid, and pyraclostrobin) [ 23 ]. To the best of our knowledge, the use of multiscale convolutional architecture for the discrimination of pesticide residue levels has not been investigated yet.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution

et al. 2022

Foods

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Pesticide residues directly or indirectly threaten the health of humans and animals. We need a rapid and nondestructive method for the safety evaluation of fruits. In this study, the feasibility of visible/near-infrared (Vis/NIR) spectroscopy technology was explored for the discrimination of pesticide residue levels on the Hami melon surface. The one-dimensional convolutional neural network (1D-CNN) model was proposed for spectral data discrimination. We compared the effect of different convolutional architectures on the model performance, including single-depth, symmetric, and asymmetric multiscale convolution. The results showed that the 1D-CNN model could discriminate the presence or absence of pesticide residues with a high accuracy above 99.00%. The multiscale convolution could significantly improve the model accuracy while reducing the modeling time. In particular, the asymmetric convolution had a better comprehensive performance. For two-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 93.68% and 95.79%, respectively. For three-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 86.32% and 89.47%, respectively. For four-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 87.37% and 93.68%, respectively, and the average modeling time was 3.5 s. This finding will encourage more relevant research to use multiscale 1D-CNN as a spectral analysis strategy for the detection of pesticide residues in fruits.

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Non-destructive detection and recognition of pesticide residues on garlic chive (Allium tuberosum) leaves based on short wave infrared hyperspectral imaging and one-dimensional convolutional neural network

Cited by 34 publications

References 38 publications

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

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

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution

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