The balance between spectral and spatial information to estimate straw cereal plant density at early growth stages from optical sensors

Estimating straw cereal plant density at early stages is important for field crop management and phenotyping. Usual plant density estimation methods include manual counting and image-based counting, both of which have limited throughput, due to the need for high spatial resolution images. In this study, we explored the potential of high-throughput estimations with spectral information. A large and diverse dataset was collected on micro plot field experiments, encompassing six sites, three leaf stages, and four species of straw cereals. Canopy spectral reflectance was acquired with a spectrometer, both in 0 degrees or 45 degrees view zenith angle, perpendicularly to the row direction. Two reflectance-based approaches were then tested. In the direct approach, density was directly estimated from reflectance using Gaussian process regression (GPR) and spectral bands selected based on Akaike's information criterion. In the indirect approach, the green fraction derived from high spatial resolution RGB images (GF_rgb) was first estimated from reflectance using GPR and selected bands, and then linearly related to density. These reflectance-based methods were compared to a classical image-based baseline method, which estimates density directly from GF_rgb. An ablation study firstly showed the superiority of 45 degrees observations, and the necessity to calibrate one model for each site, growth stage, and species. The band selection process recommended using no more than four bands as inputs to the GPR models. The resulting direct and indirect estimations had an overall relative error of 30 %. The image-based baseline method had a lower error of 22 % for submillimeter spatial resolutions, but it performed worse than reflectance-based methods when degrading the spatial resolution to more than 1 to 2 mm to mimic an increase in sensor altitude. These results showed that spectral information can compensate for spatial information and that spectral methods can potentially provide high-throughput and reasonably accurate estimates of straw cereal plant density.