Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting

Abstract

BACKGROUND Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed. RESULTS First, visible and near‐infrared (NIR‐visible) hyperspectral images were obtained. Savitzky–Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non‐embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non‐embryoid forms was 97.78% and 98.15%, respectively. CONCLUSION The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry

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.…”

“…Maize ( Zea mays L.) is one of the most widely consumed crops worldwide, and represents a major source of food, livestock feed, and industrial raw materials [ 1 , 2 ]. However, the recent, remarkable expansion of maize varieties has accompanied varietal infringement with inferior seeds or imitation varieties [ 3 , 4 ]. In addition, lax control in seed production and processing has led to adulteration of commercial varieties and a decline in seed purity, for which had been reported that every 1% reduction in seed purity would reduce maize yield by 3.7–5% [ 5 , 6 ].…”

Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

“…In the case of the Earbox, even research structures with limited resources, farmer cooperatives, or multi-site research projects (limited by multiple observers and non-standardized methodologies), can claim reliable and reproducible ear phenotyping data with a system that can be easily modified to be integrated into complete ear and grain processing chains. For example, cameras can be replaced for higher resolutions or multispectral acquisition for characterization of grain physiology [ 31 , 32 , 55 – 59 ]. Additional steps of deep learning would probably be sufficient to develop a method for the recognizing and classifying of maize diseases [ 60 , 31 , 32 ], or for characterising early grain development, by processing immature ears and grains a few days after flowering.…”

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

“…A standard whiteboard with nearly 100% reflection efficiency was used to obtain a typical white reference image. This dark current image with 0% reflectance was collected by the lamps turned off and the lens completely covered by a black cap [23]. The corrected images were calculated in accordance with Equation (1):…”