The quality of potato tubers is dependent on several attributes been maintained at appropriate levels during storage. One of these attributes is sprouting activity that is initiated from meristematic regions of the tubers (eyes). Sprouting activity is a major problem that contributes to reduced shelf life and elevated sugar content, which affects the marketability of seed tubers as well as fried products. This study compared the capabilities of three different optical systems (1: visible/near-infrared (Vis/NIR) interactance spectroscopy, 2: Vis/NIR hyperspectral imaging, 3: NIR transmittance) and machine learning methods to detect sprouting activity in potatoes based on the primordial leaf count (LC). The study was conducted on Frito Lay 1879 and Russet Norkotah cultivars stored at different temperatures and classification models were developed that considered both cultivars combined and classified the tubers as having either high or low sprouting activity. Measurements were performed on whole tubers and sliced samples to see the effect this would have on identifying sprouting activity. Sequential forward selection was applied for wavelength selection and the classification was carried out using K-nearest neighbor, partial least squares discriminant analysis, and soft independent modeling class analogy. The highest classification accuracy values obtained by the hyperspectral imaging system and was 87.5% and 90% for sliced and whole samples, respectively. Data fusion did not show classification improvement for whole tubers, whereas a 7.5% classification accuracy increase was illustrated for sliced samples. By investigating different optical techniques and machine learning methods, this study provides a first step toward developing a handheld optical device for early detection of sprouting activity, enabling advanced aid potato storage management.

马铃薯块茎的质量取决于在储存期间保持在适当水平的几种特性。这些属性之一是发芽活动，发芽活动始于块茎（眼睛）的分生组织区域。发芽活动是一个主要问题，会导致货架期缩短和糖含量升高，从而影响块茎和油炸产品的适销性。这项研究比较了三种不同光学系统的功能（1：可见/近红外（Vis / NIR）相互作用光谱，2：Vis / NIR高光谱成像，3：NIR透射率）和机器学习方法来检测基于马铃薯的发芽活性原始叶数（LC）。这项研究是在Frito Lay 1879年进行的，Russet Norkotah品种在不同温度下保存，并开发了分类模型，该模型考虑了两个品种的组合并将块茎分类为具有高或低萌芽活性。对整个块茎和切成薄片的样品进行测量，以了解其对鉴定发芽活性的影响。将顺序正向选择应用于波长选择，并使用K最近邻，偏最小二乘判别分析和软独立建模类比进行分类。通过高光谱成像系统获得的最高分类准确度值，切片和整个样本的分类准确度分别为87.5％和90％。数据融合未显示整个块茎的分类改善，而7。说明切片样品的分类精度提高了5％。通过研究不同的光学技术和机器学习方法，这项研究为开发一种用于早期检测发芽活动的手持式光学设备提供了第一步，从而可以进行先进的马铃薯存储管理。