I think OP in a way demonstrates something characteristic of Japanese agriculture:
"There are also some automatic sorters on the market, but they have limitations in terms of performance and cost, and small farms don't tend to use them."
Japanese agriculture is notoriously unproductive compared to other countries' yields and inputs, and a major reason is the lack of scale and mom-and-pop farms. If this farm were bigger or needed to save labor costs more, it could afford the already existing solutions and would sort cucumbers into more standard grades than their own ad hoc system. But they're not, so instead they do it by hand.
As an aside, my dad swore the produce was notedly better than what we have locally when he was travelling there. He on multiple occasions also discussed their dedication to quality.
Maybe they're onto something given this related discussion was on HN earlier this year...
In fact, even given their weird terrain features, they're pretty close to the top in cerial yield for example:
http://data.worldbank.org/indicator/AG.YLD.CREL.KG?end=2014&...
In some cases the shovel is better, but you can't effectively compare the two of them.
Too late: the thread you are participating in has already made the comparison from the very beginning. It's like an HN article titled "Local Man uses nuclear-heated shovel to clear snow off driveway" where the first comment is about how JCB and CAT offer superior options...
I'd argue that industrial application is much, much more advanced that current automotive AI application (because plants don't have tho choose the absolutely worst and cheapest viable solution for cost reasons as it is the case in automotive applications).
Do you have any information regarding what kind of accuracy such SVM have in practice? What do they do with the false positives and false negatives, do they reinsert back in the line ?
Fruit and vegetable sorting using computer vision is routine. That's why commercial produce is so consistent - it's been pre-sorted. The existing technologies are so fast that it's routine to inspect and sort peas, and possible to sort grains of rice.
Most of the cost and complexity in the commercial machines is in the material handling. There are conveyors and devices to get the individual items lined up. Cleaning and washing stations are usually part of the line. Commercial machines have to be built sturdily so they survive continuous use, and have to be cleanable with high-pressure soapy water. The little unit that does machine vision is a minor part of the processing line.
AI techniques are routinely used. Support vector machines are popular. One Indian system uses fuzzy logic. ANNs have been tried but are not yet popular.
The systems generally measure user-defined features - length, width, curvature, color, flaws - which then go into a simple grading algorithm. They're not trying to match human decisions.
[1] https://www.youtube.com/watch?v=5DCpOx-q_yo [2] http://www.aweta.nl/en/produce/cucumber.html