Light interception modelling using unstructured LiDAR data in avocado orchards

TL;DR

This paper introduces a solar-geometric model using LiDAR data to estimate light interception in avocado orchards, aiding pruning decisions and improving fruit production quality.

Contribution

The study presents a novel LiDAR-based, scalable model for accurately estimating light interception in individual trees for orchard management.

Findings

Model achieved R^2 = 0.854 with measured energy data.

Visual validation showed good qualitative agreement.

Model suitable for agricultural decision support systems.

Abstract

In commercial fruit farming, managing the light distribution through canopies is important because the amount and distribution of solar energy that is harvested by each tree impacts the production of fruit quantity and quality. It is therefore an important characteristic to measure and ultimately to control with pruning. We present a solar-geometric model to estimate light interception in individual avocado (Persea americana) trees, that is designed to scale to whole-orchard scanning, ultimately to inform pruning decisions. The geometry of individual trees was measured using LiDAR and represented by point clouds. A discrete energy distribution model of the hemispherical sky was synthesised using public weather records. The light from each sky node was then ray traced, applying a radiation absorption model where rays pass the point cloud representation of the tree. The model was validated using ceptometer energy measurements at the canopy floor, and model parameters were optimised by analysing the error between modelled and measured energies. The model was shown to perform well qualitatively well through visual comparison with tree shadows in photographs, and quantitatively well with R^2 = 0.854, suggesting it is suitable to use in the context of agricultural decision support systems, in future work.