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In-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)

Published: 01 February 2023 Publication History

Highlights

The use of LiDAR to segment fruit in fruit production has been confirmed.
Analysing chlorophyll-related NDVI as 3D fruit point cloud is presented.
3D fruit NDVI is correlated with spectroscopically measured NDVI.
3D NDVI point cloud of fruit in the orchard provided maturity-related chlorophyll data.
3D fruit NDVI is correlated to leaf area to fruit and fresh mass ratios.

Abstract

A feasible method to analyse fruit at the tree is requested in precise production management. The employment of light detection and ranging (LiDAR) was approached aimed at measuring the number of fruit, quality-related size, and ripeness-related chlorophyll of fruit skin.
During fruit development (65 – 130 day after full bloom, DAFB), apples were harvested and analysed in the laboratory (n = 225) with two LiDAR laser scanners measuring at 660 and 905 nm. From these two 3D point clouds, the normalized difference vegetation index (NDVILiDAR ) was calculated. The correlation analysis of NDVILiDAR and chemically analysed fruit chlorophyll content showed R2  = 0.81 and RMSE = 3.63 % on the last measuring date, when fruit size reached 76 mm.
The method was tested on 3D point clouds of 12 fruit trees measured directly in the orchard, during fruit growth on five measuring dates, and validated with manual fruit analysis in the orchard (n = 4632). Point clouds of individual apples were segmented from 3D point clouds of trees and fruit NDVILiDAR were calculated. The non-invasively obtained field data showed good calibration performance capturing number of fruit, fruit size, fruit NDVILiDAR , and chemically analysed chlorophyll content of R2  = 0.99, R2  = 0.98 with RMSE = 3.02 %, R2  = 0.65 with RMSE = 0.65 %, R2  = 0.78 with RMSE = 1.31 %, respectively, considering the related reference data at last measuring date 130 DAFB.
The new approach of non-invasive laser scanning provided physiologically and agronomically valuable time series data on differences in fruit chlorophyll affected by the leaf area to number of fruit and leaf area to fruit fresh mass ratios. Concluding, the method provides a tool for gaining production-relevant plant data for, e.g., crop load management and selective harvesting by harvest robots.

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Published In

cover image Computers and Electronics in Agriculture
Computers and Electronics in Agriculture  Volume 205, Issue C
Feb 2023
792 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2023

Author Tags

  1. Chlorophyll
  2. Digitization
  3. LiDAR
  4. Orchard
  5. Sensor
  6. Tree

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