Discriminating Robusta coffee ( Coffea canephora) cropping systems using leaf-level hyperspectral data

Abstract

The coffee agro-ecosystems are increasingly being transformed into small-scale coffee-growing agricultural systems. In this context, the challenge of accurately clas- sifying coffee cropping systems (CSs) becomes more significant, particularly in regions such as Uganda where dense vegetation and diverse topography compli- cate traditional land surveys. We harness the capabilities of remote sensing to pro- vide hyperspectral data crucial for distinguishing between various coffee CSs and other land covers. Specifically, we focus on the spectral analysis of three types of Robusta coffee CSs—those integrating agroforestry, those combined with banana cultivation, and those in full sun exposure. Using in situ hyperspectral measure- ments captured by the FieldSpec 2™ spectroradiometer across the 325 to 1075 nm range of the electromagnetic spectrum, we aimed to (1) analyze the unique spectral properties and behaviors of these Robusta coffee CSs and (2) effectively discrimi- nate among them using advanced hyperspectral datasets alongside the machine learning (ML) classification algorithms. The key to this process was the use of nar- row spectral bands (NSBs) and various narrow-band vegetation indices (VIs), serv- ing as predictor variables. A selection of critical variables (NSB = 9 and VIs = 8) was identified through the guided regularized random forest (RF) technique and then applied to four ML algorithms—RF, stochastic gradient boosting (GB), linear dis- criminant analysis, and support vector machine for classification experiments. The findings indicated high discrimination accuracy, with the RF and GB algorithms achieving overall accuracies of 93% and 90.5%, respectively, when using the selected VIs, and 87.3% (RF) and 83% (GB) when applying the chosen NBSs. These results underline the efficacy of integrating hyperspectral datasets and ML algorithms in reliably categorizing Robusta coffee CSs, a crucial step toward enhancing sustainable coffee cultivation practices.