Energy-Efficient and Economy-Sustainable Technology for Online Seed Viability Detection Using Hyper Spectrum.

Abstract

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.

Plant phenotyping relevance

マングビーン種子の生存性という植物状態を、オンライン近赤外・可視ハイパースペクトル計測とモデルで非破壊推定する手法を開発・比較検証しており、表現型取得が中心である。

Code and data availability

The paper's hyperspectral seed viability data and MATLAB analysis code are not publicly available; the Data Availability Statement explicitly states data cannot be shared, and no public repository, code URL, or trained model is provided.

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