Imagery data captured by unmanned aerial vehicles (\({\text{UAVs}}\)) have become important tools to study crop growth and development in experimental agronomic research. Moreover, growth curve analysis and nonlinear mixed-effects models (\({\text{NLME}}\)) are increasingly being used to study nonlinear crop responses in the context of repeated measurements. An \({\text{NLME}}\) to fit the Normalized Difference Vegetation Index (\({\text{NDVI}}\)) based on over-segmented \({\text{UAV}}\)-imagery of a cassava experimental field is presented. To study the parameters’ variability and error propagation, a resampling analysis and posterior \({\text{NLME}}\) fit for different sample sizes were performed. High-resolution multispectral images (7 cm resolution) were captured in a cassava experimental field sown under optimal production conditions. \({\text{NDVI}}\) for individual plants was dynamically calculated by performing a supervised classification based on over-segmented remote images using the simple linear iterative clustering algorithm for near infra-red–red–green bands. A three-parameter logistic function was adopted as a growth curve in function of growing degree days (\(^\circ {\text{C}}\)) and fitted with an \({\text{NLME}}\) approach. Inherent cassava \({\text{NDVI}}\) variability due to pixel discretization, plant architecture and cassava growth was found. On average, \({\text{NDVI}}\) values ranged from 0.35 to 0.40 for first development stages (295, 340 and 372 \(^\circ {\text{C }}\) days) until 0.77 for maturity (1606 \(^\circ {\text{C }}\) days). \({\text{NDVI}}\) plant variability was correctly addressed considering the three parameters as random effects showing small root-mean-square error. Resampling analysis proved that a suitable accuracy parameter estimation can be performed with fewer individuals (plants) which might represent an agricultural experimental design with fewer experimental units. While the application of \({\text{UAV}}\) imaging and nonlinear mixed models in determining \({\text{NDVI}}\) curves is a relevant methodological framework, further research towards the optimization of experiment sample size is required.

无人驾驶飞机（\（{\ text {UAVs}} \））捕获的图像数据已成为研究农艺实验研究农作物生长和发育的重要工具。此外，越来越多地使用生长曲线分析和非线性混合效应模型（\（{\ text {NLME}} \））研究重复测量情况下的作物非线性响应。一个\（{\ text {NLME}} \）以适合基于过度分割的\（{\ text {UAV}} \}-）图像的归一化差异植被指数（\（{\ text {NDVI}} \））介绍了木薯的实验场。要研究参数的可变性和错误传播，请进行重采样分析和后验\（{\ text {NLME}} \）进行了适合不同样本量的拟合。在最佳生产条件下播种的木薯实验田中捕获了高分辨率多光谱图像（分辨率为7 cm）。单个植物的\（{\ text {NDVI}} \）是通过使用近红外-红-绿波段的简单线性迭代聚类算法基于过度分割的远程图像执行监督分类来动态计算的。采用三参数对数函数作为生长度日数（\（^ \ circ {\ text {C}} \\））的增长曲线，并采用\（{\ text {NLME}} \）方法。发现由于像素离散化，植物结构和木薯生长导致的固有木薯\（{\ text {NDVI}} \）变异性。一般，\（{\ text {NDVI}} \）的值在第一个开发阶段（295、340和372 \（^ \ circ {\ text {C}}} \\）天）的范围从0.35到0.40，直到到期日（1606 \ （^ \ circ {\ text {C}} \）天）。\（{\ text {NDVI}} \）植物变异性已正确解决，将这三个参数视为随机效应，显示出较小的均方根误差。重采样分析证明，可以用更少的个体（植物）来执行合适的精度参数估计，这可能代表着具有更少实验单元的农业实验设计。虽然\（{\ text {UAV}} \）成像和非线性混合模型在确定\（{\ text {NDVI}} \}中的应用 曲线是一个相关的方法框架，需要进一步研究以优化实验样品的大小。