Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Abstract

The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield.

A Review on Seed Storage Technology: Recent Trends and Advances in Sustainable Techniques for Global Food Security

“…one study has proposed methods for evaluating maize seed viability. Fan et al (2023) proposed a reliable and effective method using hyperspectral imaging technology and a multi-scale 3D convolutional neural network. This method provides valuable references for agricultural applications.…”

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

“…The experimental results effectively demonstrate the effectiveness of the proposed method. Fan et al. (2023) employed hyperspectral imaging technology in combination with a multi-scale three-dimensional Convolutional Neural Network (3DCNN) to discern the vitality of individual seeds.…”

An RGB image dataset for seed germination prediction and vigor detection - maize

“…The advancement of seed vigor detection technology has raised the bar for modern agriculture. The hotspot and trend of current mainstream research is machine learning-based detection technology, which is a non-contact direct measuring method with the benefits of being direct, quick, true, and dependable ( Medeiros et al., 2020 ; Wen-ling et al., 2020 ; Sun et al, 2021 ; Tu et al., 2023 ).By using RGB to obtain corn seed images, the authors combined HSI and 3DCNN to establish an optimal classified corn seed vitality model ( Fan et al., 2023 ). In farming, measuring seed vigor is crucial, and a non-destructive machine vision method for detecting seed vigor can aid in a more accurate assessment of seed quality.…”