Estimating mango crop yield using image analysis using fruit at ‘stone hardening’ stage and night time imaging

This paper extends a previous study on the use of image analysis to automatically estimate mango crop yield (fruit on tree) (Payne et al., 2013). Images were acquired at night, using artificial lighting of fruit at an earlier stage of maturation (‘stone hardening’ stage) than for the previous study. Multiple image sets were collected during the 2011 and 2012 seasons. Despite altering the settings of the filters in the algorithm presented in the previous study (based on colour segmentation using RGB and YCbCr, and texture), the less mature fruit were poorly identified, due to a lower extent of red colouration of the skin. The algorithm was altered to reduce its dependence on colour features and to increase its use of texture filtering, hessian filtering in particular, to remove leaves, trunk and stems. Results on a calibration set of images (2011) were significantly improved, with 78.3% of fruit detected, an error rate of 10.6% and an R2 value (machine vision to manual count) of 0.63. Further application of the approach on validation sets from 2011 and 2012 had mixed results, with issues related to variation in foliage characteristics between sets. It is proposed the detection approaches within both of these algorithms be used as a ‘toolkit’ for a mango detection system, within an expert system that also uses user input to improve the accuracy of the system.