A joint research team, including researchers from University of Tsukuba, Ishida Tec Co., Ltd., and Tokyo University of Marine Science and Technology, has demonstrated a noncontact method for estimating the body weight of frozen skipjack tuna by combining three-dimensional (3D) time-of-flight imaging with machine learning. In a proof-of-concept study involving 207 frozen skipjack tuna, the researchers acquired 3D point-cloud data on a conveyor belt and extracted fork length, body height, and body width. A random forest model using all three measurements estimated body weight with a mean absolute error of 0.20 kg for fish weighing up to 7 kg, and agreement with the measured weight classes reached 80.7%, compared with 73.9% for classifications made by experienced market graders.

MIT engineers developed a tool that predicts plausible extreme events and worst-case scenarios, such as an extreme storm’s likely duration, intensity, and area of impact. Importantly, the model doesn’t need to know about previous extreme events in order to generate plausible future extreme events.