Cassava NDVI Analysis: A Nonlinear Mixed Model Approach Based on UAV-Imagery

Imagery data captured by unmanned aerial vehicles ( UAVs ) have become important tools to study crop growth and development in experimental agronomic research. Moreover, growth curve analysis and nonlinear mixed-effects models ( NLME ) are increasingly being used to study nonlinear crop responses in the context of repeated measurements. An NLME to fit the Normalized Difference Vegetation Index ( NDVI ) based on over-segmented UAV -imagery of a cassava experimental field is presented. To study the parameters’ variability and error propagation, a resampling analysis and posterior 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. 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 ( ^∘C ) and fitted with an NLME approach. Inherent cassava NDVI variability due to pixel discretization, plant architecture and cassava growth was found. On average, NDVI values ranged from 0.35 to 0.40 for first development stages (295, 340 and 372 ^∘C days) until 0.77 for maturity (1606 ^∘C days). 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 UAV imaging and nonlinear mixed models in determining NDVI curves is a relevant methodological framework, further research towards the optimization of experiment sample size is required.