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Many automated operations in agriculture, such as weeding and plant counting,require robust and accurate object detectors. Robotic fruit harvesting is oneof these, and is an important technology to address the increasing labourshortages and uncertainty suffered by tree crop growers. An eye-in-hand sensingsetup is commonly used in harvesting systems and provides benefits to sensingaccuracy and flexibility. However, as the hand and camera move from viewing theentire trellis to picking a specific fruit, large changes in lighting, colour,obscuration and exposure occur. Object detection algorithms used in harvestingshould be robust to these challenges, but few datasets for assessing thiscurrently exist. In this work, two new datasets are gathered during day andnight operation of an actual robotic plum harvesting system. A range of currentgeneration deep learning object detectors are benchmarked against these.Additionally, two methods for fusing depth and image information are tested fortheir impact on detector performance. Significant differences between day andnight accuracy of different detectors is found, transfer learning is identifiedas essential in all cases, and depth information fusion is assessed as onlymarginally effective. The dataset and benchmark models are made availableonline.

Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting