3D point cloud of normalized difference vegetation index (NDVI) of segmented fruit and leaves in apple production

Abstract A feasible method to analyse fruit at the plant considering its position, size, and maturity are requested in precise production management. The present study proposes the employment of light detection and ranging (LiDAR) to measure the position, quality-related size, and maturity-related chlorophyll of fruit. During fuit development, apples were analysed in the laboratory (n = 270) with two LiDAR laser scanners measuring at 660 and 905 nm. From the two 3D point clouds, the normalized difference vegetation index (NDVI) was calculated. The correlation analysis with chemically analysed fruit chlorophyll content showed R 2 = 0.81 and 0.02 % RMSE. The method was validated on 3D point clouds of 12 fruit trees in the orchard. Segmentation of individual apples was carried out during fruit development on five measuring dates, validated with manual rating (n = 4632). The non-invasively obtained field data showed good calibration performance capturing fruit position, fruit size, fruit NDVI of R 2 = 0.99, R 2 = 0.97, R 2 = 0.71, respectively, considering the related reference data. For 3D data of leaves, earlier shown analysis of leaf area and leaf chlorophyll by means of LiDAR was confirmed. 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 fruit ratio, as well as differences of fruit chlorophyll in different growing position at the tree. Resulting, the method provides a tool for production management, e.g. crop load management, and integration in harvest robots.