Liu Jikai, Zhu Yongji, Tao Xinyu, Chen Xiaofang, Li Xinwei
College of Resource and Environment, Anhui Science and Technology University, Fengyang, China.
Anhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Fengyang, China.
Front Plant Sci. 2022 Oct 24;13:1032170. doi: 10.3389/fpls.2022.1032170. eCollection 2022.
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.
快速准确地评估产量和氮素利用效率（NUE）对于作物生长监测、肥料的高效利用和精准管理至关重要。本研究通过使用与生育期无关的通用植被指数，探索了消费级大疆精灵4多光谱（P4M）相机在冬小麦产量或NUE评估中的潜力。通过Pearson相关分析确定了在整个生长季节与产量或NUE具有强相关性的三个植被指数，同时在不同生长时期采用基于上述植被指数的多元线性回归（MLR）、逐步MLR（SMLR）和偏最小二乘回归（PLSR）方法。累积结果表明，倒数植被指数（repRVI）在整个生长季节具有较高的产量评估潜力，灌浆后期被认为是最佳单阶段，相关系数（R）、均方根误差（RMSE）和平均绝对误差（MAE）分别为0.85、793.96 kg/ha和656.31 kg/ha。中分辨率成像光谱仪陆地叶绿素指数（MTCI）在营养生长期表现较好，在拔节期对氮素偏生产力（NPFP）的预测效果最佳，R、RMSE和MAE分别为0.65、10.53 kg产量/kg氮和8.90 kg产量/kg氮。同时，修正归一化差异蓝光指数（mNDblue）在生殖期更为准确，在灌浆后期对农学NUE（aNUE）评估的准确性最高，R、RMSE和MAE分别为0.61、7.48 kg产量/kg氮和6.05 kg产量/kg氮。此外，研究结果表明增加输入特征数量并不能提高模型精度。总体而言，这些结果表明消费级P4M相机适用于重要作物性状的早期高效监测，为精准农业系统的发展提供了一种经济高效的选择。
