Automating mango crop yield estimation

A.B. Payne, K.B. Walsh, P.P. Subedi
Several imaging technologies were assessed for determination of mango crop load, including hyperspectral and thermal imaging, but with preference given to RGB imaging. Sets of RGB images of mango fruits on trees were collected under a range of orchard conditions over five seasons. A number of different image processing algorithms were evaluated, and positive identification rates of up to 80% were achieved. Approaches included colour and texture filters, contour detection and pixel classification using various approaches. Issues that affect load estimates, occlusion of fruits by leaves and flower stalks, fruit bunching, shadowing, triggering, and insufficient lighting are discussed. These image sets are available on request for comparative work using other image processing algorithms.
Payne, A.B., Walsh, K.B. and Subedi, P.P. (2016). Automating mango crop yield estimation. Acta Hortic. 1130, 581-588
DOI: 10.17660/ActaHortic.2016.1130.87
https://doi.org/10.17660/ActaHortic.2016.1130.87
fruit, processing, colour, texture, segmentation, count
English

Acta Horticulturae