Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods

Abstract

The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications.

Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

“…ANOVA showed the ability of preprocessing methods and models to predict with a p value of p ≤ 0.05. Similar trends were also witnessed by Sohn et al [ 38 ] in studying the six different Amaranthus sp. in the fields using Vis-NIR spectroscopy coupled with modelling methods.…”

“…A total of 150 (3 × 50 = 150) spectra were collected from each group and used for further analysis. During each spectral acquisition, the Vis-NIR device’s optical window was put in direct contact with the leaf’s surface, ensuring that the sensor window was completely covered, according to the Sohn et al [ 38 ]. Background signals appeared in the raw spectra of samples due to system parameters and environmental noise.…”

“…For each of the algorithms, the inputs were provided as the data points of the spectra and the classes were the identification labels of B. napus , GM B. napus , B. rapa and F1 hybrid ( B. rapa X GM B. napus ). The metaparameters were tuned according to the Sohn et al [ 38 ] and Abdeni et al [ 46 ] for the effective use of the machine learning methods through RapidMiner software package. The classification accuracy of the various machine learning approaches combined with different preprocessing methods were calculated for identifying the precise method for the discrimination of GM and non-GM B. napus, B. rapa and F1 hybrid between B. rapa and GM B. napus.…”

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

“…A total of 300 (3 × 100 = 300) spectra were collected from each group and used for further analysis. To avoid unnecessary noise, the optical window of the Vis-NIR device was placed in direct contact with the leaf's surface throughout each spectrum capture, ensuring that the sensor window was completely covered [32,33].…”

Discrimination of Brassica juncea Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods

“…Visible near-infrared (Vis-NIR) spectroscopy is a vibrational spectroscopy technique that relies on the absorption of electromagnetic radiation in the visible and NIR range (350–2500 nm) to provide information about molecular vibrations of chemical bonds involving primary structural components of molecules [ 7 ]. This technique has been reported to discriminate plant species/varieties in various crops, such as tea [ 8 ], apple [ 9 ], peach [ 10 ], Amaranthus species [ 11 ], etc. In addition, it is used to predict oil content in soybean [ 12 ], sugar beet seed [ 13 ], sesame seed [ 14 ], and B. napus seed [ 15 ].…”