Detection of pesticide (Cyantraniliprole) residue on grapes using hyperspectral sensing

Mohite, Jayantrao; Karale, Yogita; Pappula, Srinivasu; Shabeer, T.P. Ahammed; Sawant, Sanjay D.; Hingmire, Sandip

doi:10.1117/12.2261797

Cited by 11 publications

(13 citation statements)

References 9 publications

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“…However, it should be noted that their study optimised RF and XGBoost parameters. More specifically, within viticulture, the results of our study compare favourably to those reported by [37]. The authors found that RF (87.8%) produced an improved accuracy compared to XGBoost (81.6%) when using hyperspectral data in combination with feature selection.…”

Section: Classification Using All Wavebandssupporting

confidence: 78%

“…Essentially, each tree in an XGBoost ensemble learns from previous trees and tries to reduce the error produced in subsequent iterations. Mohite et al [37] is the only known study to have employed XGBoost classification in precision viticulture. The study used hyperspectral data to detect pesticide residue on grapes.…”

Section: Study Sitementioning

confidence: 99%

“…Mohite et al [37] is the only known study to have employed XGBoost classification in precision viticulture. The study used hyperspectral data to detect pesticide residue on grapes.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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Abstract:The detection of water stress in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress. In this study, we applied two ensemble learners, i.e., random forest (RF) and extreme gradient boosting (XGBoost), for discriminating stressed and non-stressed Shiraz vines using terrestrial hyperspectral imaging. Additionally, we evaluated the utility of a spectral subset of wavebands, derived using RF mean decrease accuracy (MDA) and XGBoost gain. Our results show that both ensemble learners can effectively analyse the hyperspectral data. When using all wavebands (p = 176), RF produced a test accuracy of 83.3% (KHAT (kappa analysis) = 0.67), and XGBoost a test accuracy of 80.0% (KHAT = 0.6). Using the subset of wavebands (p = 18) produced slight increases in accuracy ranging from 1.7% to 5.5% for both RF and XGBoost. We further investigated the effect of smoothing the spectral data using the Savitzky-Golay filter. The results indicated that the Savitzky-Golay filter reduced model accuracies (ranging from 0.7% to 3.3%). The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling.

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Section: Classification Using All Wavebandssupporting

confidence: 78%

Section: Study Sitementioning

confidence: 99%

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Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

et al. 2018

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“…Studies mainly focus on the identification and detection of toxins (Chakraborty et al., 2021; Liang, Huang et al., 2020), pesticide residues (Gui et al., 2019; Mohite et al., 2017; Nie et al., 2021), and food additives (Šojić et al., 2019), as well as the estimation or prediction of the presence of heavy metals (Petrea et al., 2020; Yu et al., 2018). Nie et al.…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning to the monitoring and prediction of food safety: A review

Wang

Bouzembrak

Lansink

et al. 2021

Comp Rev Food Sci Food Safe

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Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

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“…In recent years, HSI has been widely used in nondestructive testing of fruit quality inside and outside, mainly including the external detection of fruit pest defects (Huang et al., 2013), chilling damage (Cen et al., 2016), pesticide residues (Mohite et al., 2017), fungal detection (Pieczywek et al., 2018), hardness (Erkinbaev et al., 2019), and internal detection such as internal defect (Fan et al., 2017), contents of soluble solids (Tian et al., 2019), moisture (Xu et al., 2019), and phenolic substances (Zhang et al., 2017), titratable acidity (Teerachaichayut & Ho, 2017), and pH value (Li et al., 2018). However, to our knowledge, few studies have been reported on determining the change of sugar content in lingwu jujube during storage by NIR hyperspectral imaging.…”

Section: Introductionmentioning

confidence: 99%

Determination of sugar content in Lingwu jujube by NIR–hyperspectral imaging

Yang

Liu

et al. 2021

Journal of Food Science

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Near infrared hyperspectral imaging (NIR-HSI) with a spectral range of 900 to 1700 nm was for the first time used to predict the changes of sugar content in Lingwu jujube during storage. Monte Carlo method was adopted to detect outliers, and multiple scattering correction (MSC), standard normal variate transformation (SNV), and Baseline were used to optimize modeling. Competitive adaptive reweighted sampling (CARS), interval variable iterative space shrinkage approach (iVISSA), and interval random frog (IRF) were used to select optimal wavelengths. In addition, partial least square regression (PLSR) and support vector machine (SVM) modeling based on optimal wavelengths were compared. The results showed that 30, 30, and 24 wavelengths were selected by CARS; 106, 87, and 112 feature wavelengths were selected by iVISSA; and 96, 71, and 83 optimal wavelengths were selected by IRF for sucrose, fructose, and glucose, respectively. The CARS-PLSR models provided the best results for fructose and glucose, and iVISSA-SVM model was better for sucrose. The results indicated that NIR-HSI model may be used as a rapid and nondestructive method for the determination of sugar content in jujubes.

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Detection of pesticide (Cyantraniliprole) residue on grapes using hyperspectral sensing

Cited by 11 publications

References 9 publications

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Application of machine learning to the monitoring and prediction of food safety: A review

Determination of sugar content in Lingwu jujube by NIR–hyperspectral imaging

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