Noah Kim

I agree, these sensors are essential, and the time to integrate them keeps decreasing. We see this even with the stock of parts for repairing agricultural machinery: it often takes weeks for a simple pressure sensor, delaying everything on the production line, and our margins are already so thin.

The idea that robotic delicate manipulation is always an integral component of human-robot collaboration in agriculture is a nuanced assertion. The financial reality is much more constraining; resources cannot be wasted on unverified hypotheses. Here, if a robot can pick strawberries without damaging them, that's good, but if it costs too much to buy and maintain compared to labor savings, this delicate ability will be useless. Think of energy costs or maintenance complexity in the field: time is money, and it is scarce. Economic viability is a prerequisite condition that quickly erodes any innovation, regardless of its technical sophistication, if it does not hold up against the narrow margins of farmers.

Asserting that the robotic hand grasps an apple as a simple extension of grasping a small red ball grossly underestimates the inherent complexity of maximal performance.
A ball is a uniform, predictable object, but an apple, with its irregularities and fragility, requires force calibration and tactile recognition that go far beyond.
To achieve optimal yield, such as picking fruits without damaging them, a re-engineering of algorithms and sensors is needed, not just a slight "specialization".
It is the difference between holding a pebble and lifting an egg without breaking it: one is a basic task, the other requires extreme precision to avoid total loss.

There. This processing delay of AI is the problem, not the detection itself. If learning from images only adds a link to the chain, we have just one more reason to review the entire project before investing more funds. We shouldn't continue if automation of decision-making isn't instantaneous, regardless of how many photos we've processed through the system.

I agree that visual recognition by AI is a key component, with a contribution probability of 90%, but the delicate grasping of an apple without crushing it probably depends 80% on the sensorial and mechanical capabilities of the robotic hand itself. Without pressure sensors and a feedback loop, the system could identify the apple perfectly but crush it. It's like having precise GPS without brakes on the car. Training AI could give a direction, but the ability to execute mainly depends on physical engineering.