Postharvest citrus mass and size estimation using a logistic classification model and a watershed algorithm

A machine vision system for estimation of citrus fruit mass, fruit count, and fruit size during postharvest processing was investigated with the aim of developing of an advanced citrus yield mapping system. Such yield mapping system enables the citrus growers to efficiently manage the in-grove spatial variability factors such as: soil type, soil fertility, moisture content, etc., and can help increase yield and profits. Thus, a machine vision system was developed and installed in a citrus debris cleaning machine, which removes debris from mechanically harvested loads. An image processing algorithm was developed to identify fruit from images of the postharvest citrus from a commercial citrus grove. For fruit detection, logistic regression model based pixel classification algorithms were developed. To avoid misclassification due to highly saturated area on fruit and non-fruit regions, a highly saturated area recovering (HSAR) algorithm was developed that obviated use of a “filling holes” operation. A mass calibration process was conducted, and fruit mass was estimated, which turned out to be reasonably good. The highest coefficient of determination (R2) value between the measured fruit mass and the estimated fruit mass was observed to be 0.945 and the root mean square error was 116.1 kg. A H-minima transform based watershed algorithm was used to separate the joined fruit and enabled an estimation of fruit counting and fruit size. Fruit mass estimation using the fruit size information was also conducted and these results were compared with that of the mass estimation based on fruit pixel area.