Using Green Atlas Cartographer to investigate orchard-specific relationships between tree geometry, fruit number, fruit clustering, fruit size and fruit colour in commercial apples and pears

This study investigated the adoption of a commercial, sensorised platform NDASH namely Green Atlas Cartographer, equipped with a network of proximal sensors (including cameras and LiDAR) and machine learning algorithms – to evaluate orchard-specific relationships between estimated tree geometry, fruit number, fruit clustering, fruit size and fruit colour in commercial apple and pear orchards.
The study was conducted at three commercial apple and pear sites in the Goulburn Valley (Victoria, Australia) during the 2021-2022 season.
Estimations of canopy area, canopy height, canopy density and cross-sectional leaf area were generated using LiDAR technology.
Fruit number, fruit clustering, fruit colour development and fruit diameter were estimated using a combination of machine vision and deep learning.
The geo-referenced data points generated by Cartographer were grouped into spatial plots and statistics by plot were obtained.
Correlation and principal component analyses of harvest crop parameters unveiled underlying relationships between tree geometry, productive performance and fruit quality attributes.
The relationships obtained in this study can drive orchard design strategies and dictate management decisions so that trees can be standardised to consistently produce high-quality fruit over the lifespan of modern apple and pear orchards.

XXXI International Horticultural Congress (IHC2022): III International Symposium on Mechanization, Precision Horticulture, and Robotics: Precision and Digital Horticulture in Field Environments

A. Scalisi, L. McClymont, M. Peavey, P. Morton, S. Scheding, J. Underwood, I. Goodwin

AI, ground-based platform, LiDAR, machine vision, precision horticulture

https://doi.org/10.17660/ActaHortic.2023.1360.25