Abstract: Timely and accurate monitoring of cotton growth and yield is the key to precision farming management. Unmanned Aerial Vehicle (UAV) platforms enable rapid acquisition of remote sensing data with high spatio-temporal resolution, showing great potential in crop growth parameters and yield estimation. Taking cotton in Binzhou City of Shandong Province as the research object, remote sensing images were obtained by using the multi-spectral camera installed on the UAV , and the reflectance of each band was extracted respectively, and 8 vegetation indices were screened out, and three methods such as multiple linear regression (MLR), random forest(RF) and artificial neural network (BPNN) were used to construct estimation models of cotton plant height, relative chlorophyll content and yield per plant respectively, and verified them. The results showed that the accuracy of the inversion model at the mature stage was generally higher than that at the full flowering stage. The R2 of the validation set for cotton plant height estimation in the peak flowering and mature stages were 0.842 and 0.670, respectively. The R2 values for the validation set of the chlorophyll relative content estimation model were 0.725 and 0.765, respectively. The R2 values for the validation set of the yield estimation model were 0.860 and 0.846, respectively. These results provide theoretical basis for the application of UAV remote sensing in crop growth parameters and yield estimation, and also provide a practical reference for further optimization of agricultural production management, scientific decision-making and policy formulation.

Key words: cotton, UAV remote sensing, vegetation index, plant height, SPAD, yield

摘要： 及时准确地监测棉花长势和产量是精准农业栽培管理的关键。无人机(UAV)平台能够快速获取高时空分辨率的遥感数据,在作物生长参数和产量估算方面显示出巨大的潜力。以山东省滨州市棉花为研究对象,利用安装在无人机上的多光谱相机获取遥感影像,分别提取各波段反射率,筛选出8种植被指数,采用多元线性回归(MLR)、随机森林(RF)、人工神经网络(BPNN)3种方法分别构建棉花的株高、叶绿素相对含量、单株产量的估计模型并进行验证。结果表明,基于BPNN的预测模型精度明显优于MLR和RF模型,盛花期与成熟期棉花株高估计模型验证集的R2分别为0.842和0.670;叶绿素相对含量估算模型验证集的R2分别为0.725和0.765;产量估算模型验证集的R2分别为0.860和0.846。为无人机遥感在作物生长参数与产量估算领域中的应用提供理论依据,为进一步优化农业生产管理、科学决策提供参考。

关键词: 棉花, 无人机遥感, 植被指数, 株高, 叶绿素相对含量, 产量