Guo Jinjin, Zeng Xiangtong, Ma Qichang, Yuan Yong, Zhang Nv, Lin Zhizhao, Yin Pengzhou, Yang Hanran, Liu Xiaogang, Zhang Fucang
Yunnan Key Laboratory of Efficient Utilization and Intelligent Control of Agricultural Water Resources, Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Yunnan Intelligent Water-Fertilizer-Pesticide Integration Technology and Equipment Innovation Team, Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Plants (Basel). 2025 Jun 29;14(13):1986. doi: 10.3390/plants14131986.
The rapid and accurate prediction of crop yield and the construction of optimal yield prediction models are important for guiding field-scale agronomic management practices in precision agriculture. This study selected the leaf area index (LAI) of winter wheat ( L.) at four different stages, and collected canopy spectral information and extracted vegetation indexes through unmanned aerial vehicle (UAV) multi-spectral sensors to establish the yield prediction model under the condition of slow-release nitrogen fertilizer and proposed optimal fertilization strategies for sustainable yield increase in wheat. The prediction results were evaluated using random forest (RF), support vector machine (SVM) and back propagation neural network (BPNN) methods to select the optimal spectral index and establish yield prediction models. The results showed that LAI has a significantly positive correlation with yield across four growth stages of winter wheat, and the correlation coefficient at the anthesis stage reached 0.96 in 2018-2019 and 0.83 in 2019-2020. Therefore, yield prediction for winter wheat could be achieved through a remote sensing estimation of LAI at the anthesis stage. Six vegetation indexes calculated from UAV-derived reflectance data were modeled against LAI, demonstrating that the red-edge vegetation index (CI) achieved superior accuracy in estimating LAI for winter wheat yield prediction. RF, SVM and BPNN models were used to evaluate the accuracy and precision of CI in predicting yield, respectively. It was found that RF outperformed both SVM and BPNN in predicting yield accuracy. The CI of the anthesis stage was the best vegetation index and stage for estimating yield of winter wheat based on UAV remote sensing. Under different N application rates, both predicted and measured yields exhibited a consistent trend that followed the order of SRF (slow-release N fertilizer) > SRFU1 (mixed TU and SRF at a ratio of 2:8) > SRFU2 (mixed TU and SRF at a ratio of 3:7) > TU (traditional urea). The optimum N fertilizer rate and N fertilizer type for winter wheat in this study were 220 kg ha and SRF, respectively. The results of this study will provide significant technical support for regional crop growth monitoring and yield prediction.
快速准确地预测作物产量以及构建最优产量预测模型对于指导精准农业中的田间尺度农艺管理实践至关重要。本研究选取了冬小麦在四个不同阶段的叶面积指数（LAI），通过无人机（UAV）多光谱传感器收集冠层光谱信息并提取植被指数，以建立缓释氮肥条件下的产量预测模型，并提出了实现小麦可持续增产的最优施肥策略。使用随机森林（RF）、支持向量机（SVM）和反向传播神经网络（BPNN）方法对预测结果进行评估，以选择最优光谱指数并建立产量预测模型。结果表明，在冬小麦的四个生长阶段，LAI与产量均呈显著正相关，在2018 - 2019年开花期相关系数达到0.96，在2019 - 2020年达到0.83。因此，通过在开花期对LAI进行遥感估算可实现冬小麦产量预测。根据无人机获取的反射率数据计算得到的六个植被指数与LAI进行建模，结果表明红边植被指数（CI）在估算用于冬小麦产量预测的LAI时具有更高的精度。分别使用RF、SVM和BPNN模型评估CI预测产量的准确性和精度。结果发现，RF在预测产量准确性方面优于SVM和BPNN。开花期的CI是基于无人机遥感估算冬小麦产量的最佳植被指数和阶段。在不同施氮量下，预测产量和实测产量均呈现一致趋势，顺序为SRF（缓释氮肥）> SRFU1（脲酶抑制剂和缓释氮肥按2:8比例混合）> SRFU2（脲酶抑制剂和缓释氮肥按3:7比例混合）> TU（传统尿素）。本研究中冬小麦的最佳施氮量和氮肥类型分别为220 kg·ha和缓释氮肥。本研究结果将为区域作物生长监测和产量预测提供重要技术支持。
