Image-based data collection by crews of tracked Indonesian snapper and tuna fishing vessels
Channel: Food and Agriculture Organization of the United Nations
Date: 07/10/2021

Indonesia's fisheries are mostly small-scale, species-diverse, multi-gear, and landed at many sites. The monitoring of catch composition and fishing practices is challenging. We developed an image-based catch and effort data recording system, which we tried out on Indonesia's snapper and tuna fisheries to generate data for length-based stock assessments. We worked with crews of tracked snapper and tuna vessels over 2015–2021, and we asked crew to take images of their catch with pocket cameras. We developed an AI system for automatic species identification and length measurement. Performance of AI was good, and a training dataset of 2 500 images of 44 species resulted in 95 percent accuracy for species identification. The collaborative, image-based catch monitoring program resulted in credible data for fish stock assessment, based on detailed data from 22 393 trips of 709 snapper vessels and 4536 trips for 159 tuna vessels for 2015–2020. The program was not interrupted by the covid-19 pandemic.

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