Autonomous intelligent system for fruit yield estimation
The growing global population is generating increasing demands on food production. Challenges posted by climate change and reduction in agricultural workforce make it more difficult to meet the demands. Robotics and automation are expected to promote sustainability in horticulture by increasing efficiency and reducing labour costs. This paper summarises the progress towards in-field fruit counting using the autonomous intelligent systems from The University of Sydney. The system consists of ground based robots and processing software. The focus of this study is on the application of fruit yield estimation. The robots collected image data, which were processed automatically using algorithms in a software pipeline. The first stage of the pipeline uses a generic fruit segmentation algorithm, demonstrated on apple, mango, lychee and almond orchards to classify fruit pixels in the image. The second stage performs fruit detection by finding circular clusters of fruit pixels. Finally, a fruit count estimate is produced by tallying the number of circles in images spaced at 0.5 m intervals along rows of the orchard. The estimates were compared to ground-truth yield provided by the grower after harvest, using a weighing and counting machine, and a positive correlation with R=0.81 was found.
Hung, C., Underwood, J.P., Nieto, J. and Sukkarieh, S. (2016). Autonomous intelligent system for fruit yield estimation. Acta Hortic. 1130, 545-550
image processing, fruit detection, fruit counting, machine learning, field robotics, agricultural automation