Fishing and Hunting Workers

Fishing crews already use technology but most work is still hands-on. For example, boats have sonar «fish finders» and researchers are training AI to recognize and count fish from images and sound data ^[1]. Some pilot systems even process on-deck video to “detect species, track catch and discards” in real time ^[2].

In factories, AI machines can now scan tuna with ultrasound to grade fat content much faster than people ^[3]. However, loading gear and cleaning fish largely rely on crew muscle and decisions. The U.S. O*NET database still lists “load and unload vessel equipment” and “locate fish using fish-finding equipment” as core duties ^{[4] [4]}, showing these tasks are only partly mechanized.

Hunting and fur-processing tasks see even less automation. Washing, sorting, and packing pelts by hand is still normal; only a few factory systems have begun to automate skin processing ^[1]. In short, AI is quietly helping with fish sensing and processing, but most fishing and hunting tasks remain human-driven today.

AI tools could bring benefits, but adoption may be slow for fishing and hunting. The global seafood industry is huge (around \$300 billion), so investors see opportunity ^[5]. Yet many solutions are costly.

For example, a new AI tuna-grading machine costs about ¥30 million (≈\$207,000) ^[3] – barely affordable for a small boat. On the other hand, studies suggest AI could save fuel and cut pollution on vessels ^[1], which might pay off over time if oil prices rise. Conservation groups note AI’s promise for protecting fish populations ^[6], so new regulations or subsidies could encourage its use.

In the end, tradition and cost are challenges. Fishermen and hunters value their skills – reading the sea and wildlife tracks – which AI can’t replace. A hopeful balance is that AI may take over repetitive chores (like counting or scanning), while people keep leading tasks, handling equipment, and making smart choices on the boat or in the field ^{[1] [3]}.