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基于高光谱成像和化学计量学的花生种子活力检测

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics.

作者信息

Zou Zhiyong, Chen Jie, Wu Weijia, Luo Jinghao, Long Tao, Wu Qingsong, Wang Qianlong, Zhen Jiangbo, Zhao Yongpeng, Wang Yuchao, Chen Yongming, Zhou Man, Xu Lijia

机构信息

College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China.

School of Electrical Engineering and Automation, Hubei Normal University, Huangshi, Hubei, China.

出版信息

Front Plant Sci. 2023 Feb 27;14:1127108. doi: 10.3389/fpls.2023.1127108. eCollection 2023.

Abstract

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.

摘要

花生种子活力的快速无损检测在当前研究中具有重要意义。在播种前,有效筛选用于种植的优质种子对于提高作物产量质量至关重要,而种子活力是评估种子质量的重要指标之一,它可以代表种子快速、整齐发芽并长成正常幼苗或植株的潜在能力。同时,无损检测技术的优势在于种子本身不会受到损害。在本研究中,利用高光谱技术和超氧化物歧化酶活性来检测花生种子活力。为了研究花生种子活力并预测超氧化物歧化酶活性,分析了花生种子在400 - 1000 nm波长范围内的光谱特征。光谱数据通过多种热点算法进行处理。光谱数据采用Savitzky - Golay(SG)、多元散射校正(MSC)和中值滤波(MF)进行预处理,这可以有效减少基线漂移和倾斜的影响。使用CatBoost和梯度提升决策树进行特征波段提取,花生种子活力分类特征波段的前五个权重分别为425.48nm、930.8nm、965.32nm、984.0nm和994.7nm。使用XGBoost、LightGBM、支持向量机和随机森林进行种子活力分类建模。使用XGBoost和偏最小二乘回归建立超氧化物歧化酶活性值回归模型。结果表明,MF - CatBoost - LightGBM是花生种子活力分类的最佳模型,准确率为90.83%。MSC - CatBoost - PLSR是超氧化物歧化酶活性值的最优回归模型。结果显示R为0.9787,RMSE值为0.0566。结果表明高光谱技术可以关联有效花生种子活力的外在表现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c47/10010490/5d8548399b5e/fpls-14-1127108-g001.jpg

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