Single Seed Near-Infrared Hyperspectral Imaging for Classification of Perennial Ryegrass Seed.

Abstract

The detection of beneficial microbes living within perennial ryegrass seed causing no apparent defects is challenging, even with the most sensitive and conventional methods, such as DNA genotyping. Using a near-infrared hyperspectral imaging system (NIR-HSI), we were able to discriminate not only the presence of the commercial NEA12 fungal endophyte strain but perennial ryegrass cultivars of diverse seed age and batch. A total of 288 wavebands were extracted for individual seeds from hyperspectral images. The optimal pre-processing methods investigated yielded the best partial least squares discriminant analysis (PLS-DA) classification model to discriminate NEA12 and without endophyte (WE) perennial ryegrass seed with a classification accuracy of 89%. Effective wavelength (EW) selection based on GA-PLS-DA resulted in the selection of 75 wavebands yielding 88.3% discrimination accuracy using PLS-DA. For cultivar identification, the artificial neural network discriminant analysis (ANN-DA) was the best-performing classification model, resulting in >90% classification accuracy for Trojan, Alto, Rohan, Governor and Bronsyn. EW selection using GA-PLS-DA resulted in 87 wavebands, and the PLS-DA model performed the best, with no extensive compromise in performance, resulting in >89.1% accuracy. The study demonstrates the use of NIR-HSI reflectance data to discriminate, for the first time, an associated beneficial fungal endophyte and five cultivars of perennial ryegrass seed, irrespective of seed age and batch. Furthermore, the negligible effects on the classification errors using EW selection improve the capability and deployment of optimized methods for real-time analysis, such as the use of low-cost multispectral sensors for single seed analysis and automated seed sorting devices.

Plant phenotyping relevance

単一種子のNIRハイパースペクトル画像と分類モデルを用いて、内生菌の有無および品種を識別する測定・解析手法が研究の中心であり、リアルタイム分析や自動選別への展開も示している。

Code and data availability

The supplied blocks describe NIR-HSI data acquisition and PLS-DA/ANN-DA/SVM analysis of perennial ryegrass seed, but contain no data availability statement, public dataset deposit, image repository, or author code/workflow URL. Analysis was performed in MATLAB and MIA_Toolbox (commercial software), with no public code.

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