ArticleFoods (Basel, Switzerland)2022
Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples.Foods (Basel, Switzerland) · 2026Review
- Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.Current research in food science · 2026Review
- The microbial strategies for the management of chemical pesticides: A comprehensive review.Current research in microbial sciences · 2026Review
- Nanomaterial-Enabled Spectroscopic Sensing: Building a New Paradigm for Precision Detection of Pesticide Residues.Nanomaterials (Basel, Switzerland) · 2025Review
- Machine Learning and UHPLC-MS/MS-Based Discrimination of the Geographical Origin ofFoods (Basel, Switzerland) · 2025Article
- Short-Wavelength Infrared Hyperspectral Imaging and Spectral Unmixing Techniques for Detection and Distribution of Pesticide Residues on Edible Perilla Leaves.Foods (Basel, Switzerland) · 2025Article
- Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables.Foods (Basel, Switzerland) · 2025Review
- Machine learning for predicting the prognosis of patients with thymoma and thymic carcinoma.Journal of thoracic disease · 2025Article
- Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.Foods (Basel, Switzerland) · 2025Review
- Article
- Non-destructive analysis ofFrontiers in chemistry · 2025Article
- Non-Destructive Detection of Pesticide-Treated Baby Leaf Lettuce During Production and Post-Harvest Storage Using Visible and Near-Infrared Spectroscopy.Sensors (Basel, Switzerland) · 2024Article
- Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng.Heliyon · 2024Article
- Article
- Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater.Sensors (Basel, Switzerland) · 2024Review
- Identification and Classification ofFoods (Basel, Switzerland) · 2024Article
- Study on detection of pesticide residues in tobacco based on hyperspectral imaging technology.Frontiers in plant science · 2024Article
- Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.Foods (Basel, Switzerland) · 2023Article
- Special Issue "Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches".Foods (Basel, Switzerland) · 2023Article
- Hyperspectral Imaging with Machine Learning Approaches for Assessing Soluble Solids Content of Tribute Citru.Foods (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.