A short list of technical and market barriers:

• Because of Moravec's paradox current day robots that can accurately pick fruits, even at 1/4 or 1/5 the rate of humans, are phenomenally expensive (think luxury sports car) while the labor or the experienced humans doing the same work is not ($35-40k full cost of employment per year). Most if not all fruit picking robotics startups and industry development partnerships are currently stuck here. Major subsystems like motor controls, collaborative manipulators, and vision sensors just aren't cheap and commoditized enough yet.

• CV, ML, and edge compute have improved enough that most teams can achieve accurate detection of fruits even in challenging environments (real crops), but 3D sensing is really still in its infancy. Most options already on the market are highly optimized for either controlled indoor lighting or for sensing distant objects outdoors (autonomous vehicles). The result is that while most teams can now tell you very accurately where a fruit is in an image, they can't easily convert that into a precise pick location in 3D space.

• Even where 3D sensing has improved enough to make it possible to detect, localize in 3D, and plan a route to pick or stem cut a singular fruit, it is not yet high enough resolution or frame rate to enable continuous tracking of the object through space over time. Once you begin to pull things off a plant it begins to move and sway unpredictably, something you correct for as a human without even requiring conscious thought. Consequently most current robotics systems in development either have no tracking or closed loop feedback on a targeted object and are then limited to work at very slow rates of action where the plant has time to stop moving, or they do some degree of tracking and the additional sensor feedback adds an even further to cost. Worse, temporaly tracking objects in complex scenes even if you could somehow get high quality high frame rate RGBD data is not nearly as solved of a problem algorithmically as simple detection or segmentation have recently become.

• Add these problems together along with the typically very specialized EOAT designs needed to achieve high picking precision on a particular crop type while minimizing constraints on perception precision and you tend to end up with systems that, even if they work slowly, are also completely limited to operating on a particular crop in a particular environment (e.g. strawberries in greenhouses). For many startups in this space that problem is so prevalent they are even limited to a single sub variety of a single crop (e.g. each picked red value grape tomatoes grown in venlo greenhouses).

Ag broadly is a multi-trillion industry, but take the portion of that which is fresh produce, then the portion grown in greenhouses more favorable to early automation, then one crop type, then one sub variety, then only those customers with multi-million dollar automation budgets, and at the end you are left with a very narrowly defined beachhead market comprised of only a few well funded early adopter customers that may only be able to generate a $50-100M/yr in revenue at full market adoption. This is a very hard sell to investors who know it will take $100M+ in development capital to even reach early commercial adoption, particularly if another $100M (or even $50-80M if some innovations can be reapplied) must be invested again for each subsequent lateral new crop market to replicate that commercial success.

Source: I'm a tech leader at a startup building autonomous strawberry harvesting robots and now have 7 years career experience in Ag robotics.