Freezing injury may cause irreversible damage to wheat (Triticum aestivum L) tissues and can significantly reduce yield and quality. Therefore, quick and non-destructively estimating the degree of frost damage for formulating anti-freezing protection strategies and preventing frost damage is very crucial. In this study, we obtained hyperspectral images of wheat leaves for accurate identification of frost damage. A remote-controlled Unmanned Ground Vehicle (UGV) equipped with an imaging spectral camera was used to capture the hyperspectral images of frost-damaged wheat leaves. We compared the efficiency of two methods (the one without removal of weeds, and the other is to remove the corresponding area of weeds from the hyperspectral image by Deeplab V3+) for estimation of wheat freezing damage degree by using four different algorithms; Support Vector Machine Classification (SVM), Mahalanobis Distance Classification (MaD), Minimum Distance Classification (MiD), and Maximum Likelihood Classification (ML). We found that, Deeplab V3+ can efficiently identify the weeds from hyperspectral images, as the overall accuracy (OA) values of different algorithms were high in images with weeds removal as compared to the values in weeds containing images. Further, applying ML model after weeds removal have high OA (93.26 %) as compared to the other models. Thus, using Deeplab V3+ and ML can be a potential approach to identify the freezing injury in wheat for sustainable agricultural productivity.

中文翻译：

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利用无人地面车辆 （UGV） 和高光谱成像技术实现小麦幼苗期冻害评估


冻伤可能会对小麦 （Triticum aestivum L） 组织造成不可逆的损害，并会显著降低产量和质量。因此，快速无损地估计冻害程度对于制定防冻保护策略和防止冻害至关重要。在这项研究中，我们获得了小麦叶片的高光谱图像，用于准确识别霜冻损害。配备成像光谱相机的遥控无人地面车辆 （UGV） 用于捕获霜冻受损小麦叶片的高光谱图像。我们比较了两种方法（一种不清除杂草，另一种是通过 Deeplab V3+ 从高光谱图像中去除相应面积的杂草）使用四种不同算法估计小麦冻害程度的效率;支持向量机分类 （SVM）、马氏距离分类 （MaD）、最小距离分类 （MiD） 和最大似然分类 （ML）。我们发现，Deeplab V3+ 可以有效地从高光谱图像中识别杂草，因为与包含图像的杂草相比，不同算法在去除杂草的图像中的整体准确率 （OA） 值较高。此外，与其他模型相比，在除草后应用 ML 模型具有较高的 OA （93.26 %）。因此，使用 Deeplab V3+ 和 ML 可以成为识别小麦冻害以实现可持续农业生产力的潜在方法。