Early Estimation of Olive Production from Light Drone Orthophoto, through Canopy Radius

Abstract

Background: The present work aims at obtaining an approximate early production estimate of olive orchards used for extra virgin olive oil production by combining image analysis techniques with light drone images acquisition and photogrammetric reconstruction. Methods: In May 2019, an orthophoto was reconstructed through a flight over an olive grove to predict oil production from segmentation of plant canopy surfaces. The orchard was divided into four plots (three considered as training plots and one considered as a test plot). For each olive tree of the considered plot, the leaf surface was assessed by segmenting the orthophoto and counting the pixels belonging to the canopy. At harvesting, the olive production per plant was measured. The canopy radius of the plant (R) was automatically obtained from the pixel classification and the measured production was plotted as a function of R. Results: After applying a k-means-classification to the four plots, two distinct subsets emerged in association with the year of loading (high-production) and unloading. For each plot of the training set the logarithm of the production curves against R were fitted with a linear function considering only four samples (two samples belonging to the loading region and two samples belonging to the unloading one) and the total production estimate was obtained by integrating the exponent of the fitting-curve over R. The three fitting curves obtained were used to estimate the total production of the test plot. The resulting estimate of the total production deviates from the real one by less than 12% in training and less than 18% in tests. Conclusions: The early estimation of the total production based on R extracted by the orthophotos can allow the design of an anti-fraud protocol on the declared production.

Keywords

• precision agriculture
• EVOO
• digital methods
• tree canopy
• image analysis
• yield prediction