In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality underwater images and low-identification targets present great challenges to diseased fish detection. To overcome these challenges, this paper proposes a diseased fish detection model, using an improved YOLOV5 network for aquaculture (DFYOLO). The specific implementation methods are as follows: (1) the C3 structure is used instead of the CSPNet structure of the YOLOV5 model to facilitate the industrial deployment of the algorithm; (2) all the 3 × 3 convolutional kernels in the backbone network are replaced by a convolutional kernel group consisting of parallel 3 × 3, 1 × 3 and 3 × 1 convolutional kernels; and (3) the convolutional block attention module is added to the YOLOV5 algorithm. Experimental results in a fishing ground showed that the DFYOLO is better than that of the original YOLOV5 network, and the average precision was improved from 94.52% to 99.38% (when the intersection over union is 0.5), for an increase of 4.86%. Therefore, the DFYOLO network can effectively detect diseased fish and is applicable in intensive aquaculture.

This article aims to improve the real-time monitoring accuracy of the loss rate for grain combine harvesters by optimizing the sensor-sensitive plate structure, thereby addressing the problem of low detection efficiency in existing equipment. Based on Kirchhoff’s thin plate theory, COMSOL 6.0 software was utilized to conduct modal analysis and single-grain impact tests on rectangular and circular sensing plates fabricated from three materials: stainless steel, aluminum alloy, and cupronickel. The circular stainless steel sensing plate was identified as the optimal structure, whose natural frequency and sensitivity significantly outperform those of traditional rectangular plates. By integrating a signal processing strategy based on FFT (Fast Fourier Transform) spectrum analysis (band-pass filtering: 1.0~3.0 kHz, voltage threshold: 3.5 V) and a high-level duration counting algorithm, the system effectively distinguishes between grains and impurities and resolves the counting errors caused by multi-grain impacts and secondary rebounds. Field experiments demonstrate that the developed sensor exhibits strong anti-interference ability and high measurement accuracy, providing reliable technical support for reducing harvesting losses.