Canopy Density Estimation in Perennial Horticulture Crops Using 3D Spinning Lidar SLAM

TL;DR

This paper introduces a novel 3D lidar-based method for accurately estimating vineyard canopy density using continuous-time SLAM, digital twinning, and automated canopy extraction, validated through extensive field experiments across multiple sites and seasons.

Contribution

It presents a new approach combining 3D ray cloud digital twinning with continuous-time SLAM for precise vineyard canopy density estimation, outperforming industry standards.

Findings

Achieved 3.8% repeatability in canopy density measurements.

Reduced standard deviation compared to industry gap-fraction methods.

Validated across four sites over two growing seasons with extensive data collection.

Abstract

We propose a novel, canopy density estimation solution using a 3D ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous-time 3D SLAM (Simultaneous Localisation and Mapping) to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The AgScan3D data is processed through a Continuous-Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Then, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analysed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons and 64 data collection campaigns, resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of vines with a combined total of approximately 93,000 scanned vines. Our experiments show canopy density repeatability of 3.8% (Relative RMSE) per vineyard panel, for acquisition speeds of 5-6 km/h, and under half the standard deviation in estimated densities when compared to an industry standard gap-fraction based solution. The code and field datasets are available at https://github.com/csiro-robotics/agscan3d.