As labor requirements in horticultural increase, so too does the feasibility of increased automation in these industries. This paper presents a performance evaluation of a kiwifruit harvesting robot designed to operate autonomously in pergola style orchards. The robot consists of four harvesting arms, endeffectors designed specifically for kiwifruit detachment, and a machine vision system employing convolution neural networks. Performance evaluations are presented for the harvester as a whole, as well as the machine vision system. We show the system as a whole is capable of harvesting over half of all fruit within three test orchards, equating a substantial reduction in peak harvesting labor requirements.

The growing popularity of kiwifruit orchards in New Zealand is increasing the already high demand for seasonal labourers. A novel robotic kiwifruit harvester has been designed and built to help meet this demand [H. A. Williams et al. Biosystems Eng. 181 (2019), pp. 140–156]. Early evaluations of the platform have shown good results with the system capable of harvesting 51.0% of 1,456 kiwifruit in four bays with an average cycle‐time of 5.5 s/fruit. However, the harvester's high cycle‐time, and high fruit loss rate at 23.4%, prevent it from being commercially viable. This paper presents two new developments for the harvesting system, a new vision system and two new gripper variations designed to overcome the harvester's previous limitations. Furthermore, we have designed and conducted the largest real‐world evaluation of a robotic fruit harvesting system published to date with over 12,000 kiwifruit involved. The results of this trial demonstrated that our kiwifruit harvester is one of the most effective selective fruit harvesters in the world capable of successfully harvesting 86.0% of reachable fruit, and 55.8% of all kiwifruit with a cycle‐time of 2.78 s/fruit.

There is an increasing concern that the traditional approach of natural kiwifruit pollination by bees may not be sustainable. The alternatives are currently too costly for most growers due to high labor requirements or inefficient usage of expensive pollen. This paper presents a performance evaluation of a novel kiwifruit pollinating robot designed to provide a more efficient, reliable, and cost‐effective means of producing kiwifruit. The robot comprises a novel air‐assisted sprayer, a machine vision system employing convolution neural networks, and a flower targeting system for efficient and effective application of pollen to individual flowers. We show that this pollination system is capable of individually targeting and pollinating 79.5% of flowers at 3.5 km/hr while using comparable amounts of pollen to commercial Cambrian operators. Furthermore, flowers that were successfully pollinated at 1 km/hr grew into the first robotically pollinated kiwifruit which were comparable in quality to commercially grown kiwifruit. However, the overall fruit set was found to be well below commercial requirements and further work on increasing the overall yield is required.