Effective pollination is a key challenge for indoor farming, since bees struggle to navigate without the sun. While a variety of robotic system solutions have been proposed, it remains difficult to autonomously check that a flower has been sufficiently pollinated to produce high-quality fruit, which is especially critical for self-pollinating crops such as strawberries. To this end, this work proposes a novel robotic system for indoor farming. The proposed hardware combines a 7 -degree-of-freedom (DOF) manipulator arm with a custom end-effector, comprised of an endoscope camera, a 2-DOF microscope subsystem, and a custom vibrating pollination tool; this is paired with algorithms to detect and estimate the pose of strawberry flowers, navigate to each flower, pollinate using the tool, and inspect with the microscope. The key novelty is vibrating the flower from below while simultaneously inspecting with a microscope from above. Each subsystem is validated via extensive experiments.

Global blackberry demand has been surging due to their antioxidant and nutritional value in a traditional diet. However, blackberries have extreme fragility (resulting in up to 85% of harvest batches sustaining damage) and near-ripe and ripe blackberries are difficult to distinguish in normal lighting conditions. These challenges in maintaining the blackberry supply motivate the development of an autonomous robotic solution to harvest fully ripe blackberries with minimal damage. The present paper details the mechanical design, methodology, analysis, and experimental results of a compliant robotic gripper created for this purpose. The gripper has a compact form factor and retractable fingers with specialized TPU finger pads for gentle picks, a near-infrared (NIR) reflectance-based probe for detecting full ripeness and a standardized harvesting sequence for effectively picking berries. In an outdoor harvesting experiment, the gripper attempted picking 26 berries without ripeness sensing, with 65.4% (17) being successfully picked and 38.5% (10) sustaining damage. The movements of the robot arm in the harvesting sequence were accordingly adjusted and finalized for following in-lab experiments, in which the gripper was also outfitted with ripeness sensing. Out of 40 berries, 62.5% (25) were successfully picked, with 0% of them sustaining damage. The ripeness probe classified 56 ripe and 11 near-ripe berries, with 89% (50) of the ripe and 64% (7) of the near-ripe berries being correctly classified. In a second in-lab experiment, 16 of 20 berries were successfully picked, with 2 sustaining damage.

Growing global demand for food, coupled with continuing labor shortages, motivate the need for automated agricultural harvesting. While some specialty crops (e.g., apples, peaches, blueberries) can be harvested via existing harvesting modalities, fruits such as blackberries and raspberries require delicate handling to mitigate fruit damage that could significantly impact marketability. This motivates the development of soft robotic solutions that enable efficient, delicate harvesting. This paper presents the design, fabrication, and feasibility testing of a tendon-driven soft gripping system focused on blackberries, which are a fragile fruit susceptible to post-harvest damage. The gripper is low-cost and small form factor, allowing for the integration of a micro-servo for tendon retraction, a near-infrared (NIR) based blackberry ripeness sensor utilizing the reflectance modality for identifying fully ripe blackberries, and an endoscopic camera for visual servoing. The gripper was used to harvest 139 berries with manual positioning in two separate field tests. Field testing found an average retention force of 2.06 N and 6.08 N for ripe and unripe blackberries, respectively. Sensor tests identified an average reflectance of 16.78 and 21.70 for ripe and unripe blackberries, respectively, indicating a clear distinction between the two ripeness levels. Finally, the soft robotic gripper was integrated onto a UR5 robot arm and successfully harvested fifteen artificial blackberries in a lab setting using visual servoing.