高光谱图谱结合策略检测小麦单粒种子活力

Detection of Wheat Single Seed Vigor Using Hyperspectral Imaging an d Spectrum Fusion Strategy

小麦是我国主要的粮食作物,在国民经济发展中扮演至关重要的角色。种子是一切农业活动的基础,种子活力是种子最重要的评价指标之一,高活力的种子拥有良好的田间表现及耐储能力,因此准确鉴别小麦种子活力对我国农业生产具有重要意义。传统种子活力检测技术耗时、对操作人员要求高,且会对种子造成不可逆的损伤。以往利用高光谱成像技术检测种子活力,通常是针对种子批检测,且仅仅利用图像数据或光谱数据中的一种,很少将图谱数据结合用于单粒种子活力检测。为了更深入了解种子活力与光谱的内在联系,高光谱成像的小麦单粒种子快速无损检测研究颇具学术价值。以210粒经人工老化处理过的小麦种子(105粒有活力,105粒无活力)为研究对象,采集种子400~1050 nm波段内的高光谱数据,随后进行标准发芽试验,确保高光谱数据与发芽实验结果一一对应,按照4∶2∶1的比例将数据集划分为训练集、测试集和真实数据集。利用竞争自适应重加权(CARS)算法选择特征波段,最终得到了30个特征波段,且所选特征波段对应了引起种子活力变化的蛋白质、淀粉和脂类等种子内部营养物质。为挑选出最优分类模型,对于全波段和特征波段光谱数据,利用训练集和测试集数据基于SVM、KNN、1DCNN和改进的ECA-CNN机器学习算法分别建立了小麦种子活力预测模型。结果表明,使用特征波段数据建立的模型性能均优于使用全波段数据建立的模型,其中使用特征波段数据建立的ECA-CNN模型性能最好,在避免过拟合的情况下,训练集整体准确率为99.17%,测试集准确率为80%。为避免建模过程对比较分类策略造成影响,利用真实数据集对比整体法和像素法两种分类策略。结果表明,像素法相比于整体法拥有更好的检测效果,整体准确率为86.67%,精确率为92.31%,召回率为80%,均优于像素法。该研究可为快速无损检测单粒小麦种子活力提供科学依据。

Wheat is a primary staple crop in China and is pivotal in the nation's economic development.Seeds form the foundation of all agricultural activities,with seed vigor being one of the most crucial evaluation indicators.Seeds with high vigor exhibit superior field performance and storage resilience.Thus,accurately identifying wheat seeds'vigor is paramount to China's agricultural production.Traditional seed vigor detection techniques are time-consuming,demand expertise,and can irreversibly damage the seeds.Previous attempts to detect seed vigor using hyperspectral imaging technology typically focused on batch testing of seeds,utilizing either image data or spectral data,but rarely combining both for single seed vigor detection.This study explores the potential of hyperspectral imaging technology for rapid,non-destructive detection of individual wheat seeds.A total of 210 manually aged wheat seeds(105 viable,105 non-viable)were studied.Hyperspectral data within the seeds'400~1050 nm band were collected,followed by a standard germination test to ensure a one-to-one correspondence between the hyperspectral data and germination results.The dataset was divided into training,testing,and real datasets in a 4∶2∶1 ratio.The Competitive Adaptive Reweighted Sampling(CARS)algorithm was employed to select feature bands,resulting in 30 feature bands corresponding to seed nutrients like proteins,starch,and lipids influencing seed vigor.To identify the optimal classification model,prediction models for wheat seed vigor were established using support vector machine(SVM),k-nearestneighbor(KNN),one-dimensional convolutional neural network(1DCNN),and the improved ECA-CNN machine learning algorithms,based on both full-band and feature-band spectral data from the training and testing sets.The results indicated that models built using feature-band data outperformed those using full-band data.The ECA-CNN model,constructed with feature band data,exhibited the best performance,achieving an overall accuracy of 99.17%for the training and 80%for the testing sets.The overall method and pixel method classification strategies were compared using the real dataset to negate the influence of modeling processes on comparison strategies.The findings revealed that the pixel method surpassed the overall method in detection efficacy,with an overall accuracy of 86.67%,a precision of 92.31%,and a recall rate of 80%.This research offers theoretical support for the rapid,non-destructive detection of individual wheat seed vigor.