Estimation of the structural parameters of an agroforestry system using very high-resolution drone images

Cocoa-based agroforestry systems are designed to improve production and mitigate the adverse effects of climate change. The objective of this study is to enhance our understanding of how to determine the structural parameters of cocoa-based agroforestry systems using drone imagery. To this end, images of ten cocoa plantations were acquired with a Phantom 4 multispectral drone to determine structural parameters such as density, canopy area, and the height of trees associated with the cocoa trees. Visual analysis of the orthophotos identified five land-use types: banana trees, palm trees, other trees, bare soil, and an "other" category encompassing cocoa trees, cashew trees, and shrubs of equal or smaller size than the cocoa trees. Analysis of the results reveals that structural parameters, such as tree density and canopy area obtained from the drone orthophotos, increase with the age of the cocoa plantations. Comparing tree heights measured by drone with those measured on the ground demonstrates the drone's accuracy for this purpose. This tool could therefore be used to assist producers in converting their cocoa plantations to agroforestry systems that comply with current standards, while simultaneously reducing the workload associated with diagnostics.