首页|Studies Conducted at Baylor University on Machine Learning Recently Reported (Analysis of an Optical Imaging System Prototype for Autonomously Monitoring Zooplankton In an Aquaculture Facility)
Studies Conducted at Baylor University on Machine Learning Recently Reported (Analysis of an Optical Imaging System Prototype for Autonomously Monitoring Zooplankton In an Aquaculture Facility)
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NETL
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Elsevier
Current study results on Machine Learning have been published. According to news originating from Waco, Texas, by NewsRx correspondents, research stated, “Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms.” Financial support for this research came from United States Department of Energy (DOE). Our news journalists obtained a quote from the research from Baylor University, “Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate.”
WacoTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningTechnologyBaylor University
NETL
NSTL
Elsevier
