Haploid breeding can shorten the breeding period of new maize varieties and is an important means to increase maize yield. In the breeding program, a large number of haploid seeds need to be screened, and this step is mainly achieved manually, which hinders the industrialization of haploid maize breeding. This article aims to develop a multispectral camera to identify the haploid seeds automatically. The camera was manufactured by replacing narrow-band filters of the ordinary CCD camera, and the RGB, 405 nm, 980 nm and 1050 nm images of haploid or diploid seeds were simultaneously captured (the characteristic wavelengths were determined according to color and high-oil markers of maize). The performance was tested using four maize varieties with the two genetic markers. The results show that the developed multispectral camera significantly improved the recognition accuracy of haploid maize seeds to 92.33%, 97.33%, 97% and 93.33% for the TYD1903, TYD1904, TYD1907 and TYD1908 varieties, respectively. The cameras in the near-infrared region (wavelengths of 980 nm and 1050 nm) achieved better performance for the varieties of high-oil marker, with an increase of 0.84% and 1.5%, respectively. These results demonstrate the strong potential of the multispectral imaging technology in the haploid seed identification of maize.

The seed viability detection before sowing is indispensable in the agricultural production of mung beans. The conventional detection methods for seed viability are destructive, carry a risk of contamination, and fail to identify individual non-viable seeds. In this study, an efficient and sustainable method for online viability detection of mung bean seeds was developed, which utilized hyperspectral techniques and had characteristics of rapid speed, non-destructive analysis, and the ability to detect the viability status without pollution to the environment. A sample holder for mung bean seeds was designed to stably collect spectral data. The effects of different optimal spectral bands and modeling algorithms on the detection accuracy of seed viability were analyzed. Compared to the support vector machine (SVM) and the extreme learning machine (ELM) algorithms, the partial least squares (PLS) algorithm based on the visible and near-infrared spectra (380~980 nm) had better performance. The accuracy for the identification of non-viable seeds was 98.8%, and the error of viability prediction was 20.71%. The cost of a one-time viability test is $0.25 with energy consumption of 0.05 kWh⁻¹, which is much lower than the germination test with a cost of $80.2 and energy consumption of 50.4 kWh⁻¹. Furthermore, individual non-viable seeds can be identified and removed, and the revenue increases by $286.9 per hectare after sorting the non-viable seeds from the seeds with an 85% germination rate. This will promote the cleaner production of mung beans without additional chemical solutions added in the process.