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

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

doi:10.1117/12.2261797

1 May 2017 Detection of pesticide (Cyantraniliprole) residue on grapes using hyperspectral sensing

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

Proceedings Volume 10217, Sensing for Agriculture and Food Quality and Safety IX; 102170P (2017) https://doi.org/10.1117/12.2261797
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

Pesticide residues in the fruits, vegetables and agricultural commodities are harmful to humans and are becoming a health concern nowadays. Detection of pesticide residues on various commodities in an open environment is a challenging task. Hyperspectral sensing is one of the recent technologies used to detect the pesticide residues. This paper addresses the problem of detection of pesticide residues of Cyantraniliprole on grapes in open fields using multi temporal hyperspectral remote sensing data. The re ectance data of 686 samples of grapes with no, single and double dose application of Cyantraniliprole has been collected by handheld spectroradiometer (MS- 720) with a wavelength ranging from 350 nm to 1052 nm. The data collection was carried out over a large feature set of 213 spectral bands during the period of March to May 2015. This large feature set may cause model over-fitting problem as well as increase the computational time, so in order to get the most relevant features, various feature selection techniques viz Principle Component Analysis (PCA), LASSO and Elastic Net regularization have been used. Using this selected features, we evaluate the performance of various classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to classify the grape sample with no, single or double application of Cyantraniliprole. The key finding of this paper is; most of the features selected by the LASSO varies between 350-373nm and 940-990nm consistently for all days. Experimental results also shows that, by using the relevant features selected by LASSO, SVM performs better with average prediction accuracy of 91.98 % among all classifiers, for all days.

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Jayantrao Mohite, Yogita Karale, Srinivasu Pappula, Ahammed Shabeer T. P., S. D. Sawant, and Sandip Hingmire "Detection of pesticide (Cyantraniliprole) residue on grapes using hyperspectral sensing", Proc. SPIE 10217, Sensing for Agriculture and Food Quality and Safety IX, 102170P (1 May 2017); https://doi.org/10.1117/12.2261797

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