Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Highlights

The automatic detection and recognition of farming action in video are realized.

Abstract. In aquaculture, regular "inspection" and "applying pesticides" are essential to improving production efficiency and fish disease treatment, but the current aquaculture system does not effectively support these strategies. Therefore, this paper proposes a farming action recognition network (FARnet), which can accurately locate the farmers in the video and detect the actions of “applying pesticides” and “inspection.” The dataset was captured and produced by multi-angle cameras, which were consulted with relevant experts. In this network, Coordinate Attention (CA) was used to improve the Efficient Layer Aggregation Networks-tiny (ELAN-tiny) and Spatial Pyramid Pooling (SPP) structures in the YOLOv7-tiny network. The precise implementation methods are as follows: (1) The convolution in ELAN-tiny was replaced with the CA module, and a shortcut was added. (2) A CA module was added to the final layer of the Spatial Pyramid Pooling (SPP) module. (3) The improved Efficient Layer Aggregation Networks-Coordinate Attention (ELAN-CA) and Spatial Pyramid Pooling-Coordinate Attention (SPP-CA) were used to extract action features and perform feature correction by ADD (Feature fusion by feature map summation) in the backbone. The results demonstrated that the FARnet achieved significantly better detection results than the YOLOv7-tiny network, where mAP@.5 improved by 0.1% from 99.4% to 99.5%, and the mAP@.5:.95 improved by 6.6% from 78.2% to 84.8%. Therefore, the FARnet can effectively detect and identify the “inspection” and “applying pesticides” actions of farmers and provide useful input information for the intelligent management system.