Abstract
This paper presents a low-cost and cloud-based autonomous drone system to survey and monitor aquaculture sites. We incorporated artificial intelligence (AI) services using computer vision and combined various deep learning recognition models to achieve scalability and added functionality, in order to perform aquaculture surveillance tasks. The recognition model is embedded in the aquaculture cloud, to analyze images and videos captured by the autonomous drone. The recognition models detect people, cages, and ship vessels at the aquaculture site. The inclusion of AI functions for face recognition, fish counting, fish length estimation and fish feeding intensity provides intelligent decision making. For the fish feeding intensity assessment, the large amount of data in the aquaculture cloud can be an input for analysis using the AI feeding system to optimize farmer production and income. The autonomous drone and aquaculture cloud services are cost-effective and an alternative to expensive surveillance systems and multiple fixed-camera installations. The aquaculture cloud enables the drone to execute its surveillance task more efficiently with an increased navigation time. The mobile drone navigation app is capable of sending surveillance alerts and reports to users. Our multifeatured surveillance system, with the integration of deep-learning models, yielded high-accuracy results.
Highlights
Food security and affordability are some of the major concerns of the world’s growing population
We propose a low-cost and cloudbased autonomous drone equipped with a single camera to perform surveillance
The power of drone navigation was designed based on the concept of an eagle eye, capable of flying above the aquaculture site to perform area surveillance
Summary
Food security and affordability are some of the major concerns of the world’s growing population. It is a challenge to increase food production and make food more affordable and accessible to the general public. Many depend on fisheries, in fish farming or aquaculture, as a food source. Fish farming was a small-scale production that only addressed the need for a family or a small community’s livelihood source. The increasing demand for aquaculture industrialization pushed small-scale farmers to improve their farming skills to increase production in their operations. One of the challenges to aquaculture property security is theft and human intrusions that can affect profit and farm operations, to larger aquaculture sites with more financial investments and resources. Aside from security threats, fish welfare and fish feeding behaviors are significant factors for successful fish farming. Fish behaviors have practical and economic significance
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