利用数字图像处理技术检测巨斑期蟹幼虫

Novi Nurlaela, M. Niswar, I. Nurtanio, Y. Fujaya, S. Kashihara, Doudou Fall
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引用次数: 1

摘要

青蟹作为出口商品具有很高的经济价值。蟹类养殖中出现的一个问题是在幼虫发育阶段同类相食。由于摄食不足,大月相蟹幼虫容易吃小月相蟹幼虫。为避免在蟹幼虫发育阶段同类相食,养殖户需要定期检查蟹幼虫的发育情况,并将大鳞期蟹幼虫与小鳞期蟹幼虫分开。检查是通过直接观察饲养槽来完成的,这既耗时又困难,因为螃蟹幼虫的阶段变化是不可预测的。本文主要研究了卷积神经网络(CNN)方法对巨斑期蟹幼虫的检测过程。本研究使用了210张图像,检测结果显示平均准确率为87.7%。
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Detection of Megalopa Phase Crab Larvae Using Digital Image Processing
Blue Swimmer Crab has high economic value as an export commodity. One of the problems that occur in crab culture is cannibalism during the larval development phase. Megalopa phase crab larvae tend to eat zoea phase crab larvae due to inadequate feeding. To avoid cannibalism in the crab larvae development phase, farmers need to check the development of larvae regularly and separate the megalopa phase crab larvae from zoea phase crab larvae. Checking is done by looking directly at the rearing tank, which is time-consuming and difficult because the phase changes in the crab larvae are unpredictable. This research focuses on the detection process of the megalopa phase crab larvae using a convolutional neural network (CNN) method. This research uses 210 images, and detection results get shown an average accuracy of 87.7%.
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