Detecting, mapping and digitising canopy geometry, fruit number and peel colour in pear trees with different architecture

Increasing farm labour and input costs and the requirement for more orchard data are leading to rapid advances in technology to improve management systems in fruit production. The study aimed to (i) validate a sensorised platform to estimate fruit number, peel colour and blush coverage in pear orchards with different pear selections and tree architectures, (ii) establish relationships between fruit number, peel colour and canopy geometry features, and (iii) evaluate the platform for mapping and digitising orchard features. The study was carried out over two years in an experimental 'ANP-0131 ' orchard and in one year in two commercial pear orchards ('ANP-0131 ' and 'PremP009'). Predictions of fruit number and blush coverage were compared in traditional three-dimensional (3D) and modern high-density two-dimensional (2D) training systems. Overall, prediction errors for fruit number were < 6.5 % in all the training systems, but improved performance was achieved in vertical 2D configurations (% standard errors = 2.2 %). Fruit number and blush coverage per unit of leaf area were higher in 2D compared to 3D training systems. Blush coverage predictions in 'ANP-0131 ' were reliable (R-2 = 0.67, RMSE = 3.70 %). Accurate predictions of blush coverage classes were achieved by modifying sample variance. Fruit number and blush coverage were negatively affected by increasing canopy size. The platform proved useful for mapping and digitisation. Spatial heatmaps of orchard features provided a valuable visual aid to identify zones for priority interventions for peel colour enhancement.