Identification of Rice Seed Varieties Based on Near-Infrared Hyperspectral Imaging Technology Combined with Deep Learning

Abstract

Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300−1400 nm and secondarily concentrated at approximately 1050−1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

“…The range of 900–980 nm was contributed to by the third overtone of C-H relevant to sugar [ 55 ]. For NIR spectra, wavelengths between 1050 nm and 1200 nm are mainly made up of the second overtone of C−H, and those between 1300 nm and 1500 nm are mainly related to the frequency of C-H [ 56 ]. The range of 1210–1450 nm is attributed to the 2nd overtone of C-H and the 1st overtone of O-H [ 54 ].…”

“…Previous studies have used SVM [ 22 , 59 ], DT [ 59 ], KNN [ 59 ], or RF [ 18 ] to detect pesticide residue, which showed fine results. SVM [ 60 , 61 , 62 ], LR [ 63 ], CNN [ 31 , 56 , 64 ], RF [ 60 , 62 ], and ResNet [ 56 ] have been applied widely in quality detection of hyperspectral imaging. In this study, classic machine learning and deep learning methods, CNN, ResNet, LR, SVM, and RF, were used to achieve a multivariate analysis of the detection of pesticide residue levels in grapes.…”

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

“…Previous studies have researched the non-destructive identification of seed varieties based on hyperspectral imaging and machine learning or deep learning [ 4 , 9 , 17 , 18 , 24 – 26 , 48 , 49 ]. Although there might be a certain distance from the actual application due to a limited number of varieties in the training set, the successes of these studies guide the seed variety genuineness detection to ensure seed purity.…”

“…These studies show that the SVM and deep learning models are effective algorithms for processing large phenotypic spectral datasets. Even though the convolutional neural network (CNN) performs slightly better than the SVM in some cases, their overall performances are very close [ 49 ]. So, we chose the traditional machine learning algorithms (SVM, MLP, and RF) and got detection accuracy above 99% (SVM and MLP) that can be widely adopted, especially in resource-limited agricultural settings.…”

Continuous Monitoring Analysis of Rice Quality in Southern China Based on Random Forest

“…Te results showed that it could reduce the average absolute error and root mean square error. Jin et al [7] used the random forest algorithm to identify rice varieties. de Santana et al [8] used random forest and infrared spectra to detect food adulteration.…”