An image-based method to study the fruit tree canopy and the pruning biomass production in a peach orchard

Abstract

The feasibility of two nondestructive methods based on image processing techniques was assessed for fruit tree research. The methods were evaluated in a 2-year (2011 and 2012) field experiment, during which various irrigation and soil management treatments were set up in a commercial peach orchard. Canopy image analysis was conducted using two approaches, namely the orthoimage and the lateral image technique. The proposed methods were compared with other classical measurements such as trunk diameter (TD) increase and pruning weight (PW). Orthoimage canopy area (OCA) analysis resulted in a reliable and sensitive technique to study the active crop growth along the growing season. The OCA values obtained were highly correlated with TD measurements (r2 = 0.88), thus describing an exponential significant model (y = 0.0997 e0.0521x). Cumulative crop growth was determined using the virtual pruning (VP) technique. VP estimates were well correlated with fruit tree PWs during 2011 (r2 = 0.86) and 2012 (r2 = 0.80). The nondestructive image-based techniques proved sensitive to crop growth and useful for the study of fruit tree canopies. On the basis of our results, we conclude that the proposed image analysis methods are valuable new approaches with wide applications in fruit tree research.