Nanovel Unveils Plans for Its Robotic Tree Fruit Harvester
December 3, 2022 
Israel-based Nanovel expects to launch a beta version of its autonomous tree fruit harvester in 2023, according to Founder and CEO Isaac Mazor. The debut will occur in the citrus market, although the technology will also apply to stone fruit, avocado, mango, and other tree fruit.
“Nanovel is committed to address a serious threat to the future supply of tree fruit. Enhancing grove profitability is a driving motivator behind building our new AI-driven robotic harvester,” Mazor says. “Nanovel’s robot is extremely intelligent, ag-environment durable, and cost-effective. Grower input throughout the development process sharpens the Nanovel team’s focus on solving real-world problems using our proprietary technology. Our mission is to provide growers a reliable, productive, and affordable solution for tree fruit harvesting.”
Founded in 2018, Nanovel is a pioneer in the field of autonomous harvesting of deep foliage tree fruit. Its proprietary solution combines AI, computer vision, machine learning, and proprietary robotics technologies to enable autonomous tree fruit harvesting to become more productive, reliable, and cost effective than hand harvesting.
“Farm labor shortage is one of the main challenges we are facing today,” says Tal Fogelman, a California grower and Business Development Advisor to Nanovel, says. “Especially around harvest time, farm labor is scarce, challenging to secure. Nanovel’s solution will be serving a multi-billion-dollar market that is currently relying on the traditional method of manual picking.”
The autonomous tree fruit harvester will provide multiple benefits to growers, according to the company:
- Deep foliage real-time “find and pick” capability enabled by edge vision and computing.
- Supports gripping and trimming using proprietary technology suitable for a wide variety of fruit types: citrus, stone fruit, avocado, mango, and more.
- Reliable harvest scheduling.
- Valuable insights of yield and quality by big data collection and analysis.
- Cost-competitive compared to traditional manual picking.
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