Evaluation and demonstration of precision management practices in pear orchards

Precision agriculture in 'three dimensional' (3D) crops such as pear trees poses many challenges. It is unclear to what extent results and methods from 'two dimensional' (2D) crops can be extrapolated to 3D crops. Therefore, several techniques used in 2D precision agriculture, including soil electrical conductivity (EC) scanning and drone imaging, were tested in two pear orchards. Soil characteristics, crop growth and fruit yield were monitored simultaneously in the field. Significant correlations were found between the number of white (flower) pixels in drone images taken during bloom, the number of flower clusters counted per tree and the final fruit yield. Furthermore, multivariate analysis indicates that soil EC and common vegetation indices derived from drone images such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE) can be used as estimators of crop growth, fruit yield and quality. These findings suggest that a predictive model and guidelines for differentiated orchard management might be designed to estimate and steer fruit yield based on flower intensity mapping, soil EC scanning and common vegetation indices.