| dc.description.abstract |
Coffee is one of the major agricultural produce popular worldwide. Coffee harvesting is performed in two ways (a) Selective harvesting - in which only ripe coffee cherries are picked, leaving the unripe coffee cherries intact. (b) Strip harvesting - in which the cherries are stripped out without separation of ripe and unripe ones. Although this can be completed quickly, this results in higher percentages of unripe, which reduce the quality and sale value, resulting in less profit for producers. The selective coffee cherry harvester should identify and distinguish ripe and unripe cherries and hence a fully automated harvesting system should be vision-guided. The design of developing a vision-based harvesting system for coffee cherries is particularly difficult due to the size of the coffee cherries, the clustered arrangement of the coffee cherries, and the height of the coffee plant. Currently available harvesters are based on strip harvesting and hence there is a need to develop harvestors for selective harvesting of coffee cherries. In this work, we present cherry plucking strategies for a selective coffee harvester robot. This analysis is one of the key work required towards the implementation of the vision guided-selective harvester. The proposed work is tested in simulation as well as on hardware consisting of Interbotix ReactorX150 robot arm and Intel Realsense 435i camera. |
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