Seagrass ecosystems play a vital role in maintaining marine biodiversity and ecological balance, making their monitoring and management essential. This study proposes a novel approach for clustering of seagrass images into three distinct age categories: young, medium, and old, using deep learning and unsupervised machine learning techniques. VGG-16 convolutional neural networks (CNN) are employed for feature extraction from the seagrass images, followed by K-means clustering to categorize the image samples into the specified age groups. The implemented methodology begins with the collection and annotation of a diverse seagrass image dataset, including samples from various locations and conditions. Images are first pre-processed to ensure consistent size and quality. To enable real-time capabilities, an optimized VGG-16 CNN is then fine-tuned on the annotated dataset to learn discriminative features that capture age-related characteristics of the seagrass leaves within the constraints of real-time image processing. After feature extraction, the Kmeans clustering algorithm is applied to group the images into young, medium, and old categories based on the learned features. The clustering results are evaluated using quantitative metrics such as the silhouette score and Davies-Bouldin index, demonstrating the effectiveness of the proposed method in capturing age-related patterns in seagrass imagery. This research contributes to the field of seagrass monitoring by providing an automated and real-time approach to classifying seagrass images into age categories which can facilitate more accurate assessments of seagrass health and growth dynamics. A real-time capability would equip decision-makers with a valuable tool for immediate responses and support the sustainable management of seagrass ecosystems in various marine environments.
KEYWORDS: Video, Unmanned aerial vehicles, Image segmentation, Image processing, Video processing, Turbidity, Fourier transforms, Calibration, Video coding, RGB color model
Sediment plumes are generated from both natural and human activities in benthic environments, increasing the turbidity of the water and reducing the amount of sunlight reaching the benthic vegetation. Seagrasses, which are photosynthetic bioindicators of their environment, are threatened by chronic reductions in sunlight, impacting entire aquatic food chains. Our research uses unmanned aerial vehicle (UAV) aerial video and imagery to investigate the characteristics of sediment plumes generated by a model of anthropogenic disturbance. The extent, speed, and motion of the plumes were assessed as these parameters may pertain to the potential impacts of plume turbidity on seagrass communities. In a case study using UAV video, the turbidity plume was observed to spread more than 200 ft over 20 min of the UAV campaign. The directional speed of the plume was estimated to be between 10.4 and 10.6 ft/min. This was corroborated by observation of the greatest plume turbidity and sediment load near the location of the disturbance and diminishing with distance. Further temporal studies are necessary to determine any long-term impacts of human activity-generated sediment plumes on seagrass beds.
Sediment plumes are generated from both natural and human activities in benthic environments, increasing the turbidity of the water and reducing the amount of sunlight reaching the benthic vegetation. Seagrasses, which are photosynthetic bioindicators of their environment, are threatened by chronic reductions in sunlight, impacting entire aquatic food chains. This research uses UAV aerial video and imagery to investigate the characteristics of sediment plumes generated by a model of anthropogenic disturbance. The extent, speed and motion of the plumes were assessed as these parameters may pertain to the potential impacts of plume turbidity on seagrass communities. In a case study using UAV video, the turbidity plume was observed to spread over 250 feet over 20 minutes of the UAV campaign. The directional speed of the plume was estimated to be between 10.4 and 10.6 ft/min. This was corroborated by observation of greatest plume turbidity and sediment load near the location of disturbance and diminishing with distance. Further temporal studies are necessary to determine long-term, if any, impacts of human activity-generated sediment plumes on seagrass beds.
Segmentation of individual seagrass images is of importance to biologists who are investigating individual seagrass blade cover to correlate the surface cover information to benthic environmental factors. Seagrasses may be covered with epiphytes like crustose and filamentous algae and tubeworms, all bioindicators of nutrient and turbidity conditions of the seagrass environment. Classical image processing techniques to segment seagrasses have been successful; however, such techniques are relatively time consuming. We introduce deep learning as a computationally efficient approach to perform semantic segmentation in multiple seagrass images to determine each blade’s percent cover and surface composition. Pre-trained ResNet-18 and ResNet-50 convolutional neural networks have been adapted using transfer learning to classify seagrass blade surface composition. Seagrass surface semantic segmentation and mapping is achieved for five classes including the bare seagrass blade (no cover), general epiphyte, tubeworm, filamentous algae, and background. We present the application of deep learning in two convolutional neural networks to achieve semantic segmentation of seagrass blades as a fast tool for seagrass surface classification. Classification accuracy and computational performance of the two deep CNN are presented.
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