Abstract:
Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.
摘要:
水产养殖需要精确的非侵入性方法来估算生物量。这项研究验证了一种新颖的计算机视觉方法,该方法使用基于特征函数的特征提取算法,该算法结合了鱼类大小和形状的统计形态学分析和机器学习,以提高鱼塘生物量估计的准确性,并专门应用于罗非鱼(Oreochromisniloticus)。通过比较在两种不同照明条件下应用于三种常见机器学习方法时的结果,将从图像中自动提取的这些特征与先前手动提取的特征进行测试。该分析的数据集包括129个罗非鱼样品。结果给出了有希望的结果,因为多层感知器模型显示出稳健的性能,在不同的功能和照明条件下始终表现出卓越的准确性。模型的可解释性质,根植于签名函数的统计特征,可以提供对不同发育阶段的形态和异速变化的见解。对现有文献的比较分析强调了拟议方法的竞争力,指向精度的进步,可解释性,和物种多功能性。这项研究为该领域做出了重大贡献,加速寻求非侵入性鱼类生物识别技术,这些生物识别技术可以在不同发展阶段的各种水产养殖物种中推广。结合检测,跟踪,和姿势识别,诸如最新研究中提供的深度学习方法可以为实时鱼类形态发育提供强大的方法,生物量估算,和福利监测,这对养鱼场的有效管理至关重要。