A major part of supplying the increasing demand for seafood around the world is aquaculture. This work suggests a novel deep learning-based method for automated feed management to improve efficiency. Conventional aquaculture feeding methods frequently depend on manual supervision and recurring feeding schedules, which can have an adverse effect on the environment and result in an inadequate use of resources. By using less resources and addressing environmental issues, this technology not only increases aquaculture productivity, but also promotes sustainable practices. In conclusion, there is a lot of potential for aquaculture operations to be revolutionised by the suggested automated feed management system that uses CNNs (convolution neural networks) and deep learning. It solves long-standing feed management inefficiencies by fusing real-time data analysis with adaptive decision-making, opening the door for a more fruitful and sustainable future for the aquaculture sector.
Top1. Introduction
Aquaculture, the cultivation of aquatic organisms such as fish, shellfish, and aquatic plants, has become a critical industry in meeting the increasing global demand for seafood. As the aquaculture sector continues to expand, so does the need for innovative technologies to enhance efficiency, productivity, and sustainability (Hu et al., 2022). One area ripe for improvement is feed management, a crucial aspect of aquaculture operations. Traditional feed management practices often rely on manual observation and fixed feeding schedules, leading to suboptimal resource utilization and potential environmental concerns (Yang et al., 2021).
In recent years, the integration of deep learning technologies has emerged as a transformative approach to address challenges in various domains, and aquaculture is no exception (Sun et al., 2020). This study focuses on the application of Convolutional Neural Networks (CNNs), a subset of deep learning, to automate and optimize feed management in aquaculture. CNNs, originally designed for image-related tasks, have shown remarkable success in image classification, object detection, and segmentation, making them well-suited for analyzing aquatic environments and the behavior of aquatic organisms (Gladju et al., 2022).
The successful use of resources, especially feed, is critical to aquaculture. Feeding schedules based on manual observations are part of traditional feed management, which can result in either overfeeding or underfeeding, both of which have detrimental effects. While underfeeding can impair aquatic animals' growth and health, overfeeding leads to wasted feed, higher production costs, and environmental contamination (Zhao et al., 2021). Furthermore, it can be difficult to successfully execute fixed feeding schedules due to the dynamic character of aquatic ecosystems and the fluctuation of environmental conditions (Wang et al., 2022).
These issues are addressed by the use of deep learning, and more especially CNNs, which offer an adaptable, data-driven feed management system (Chiu, 2022), (Li et al., 2020). CNNs are excellent at identifying hierarchical characteristics in visual data, which makes them a good choice for examining photos and videos taken in aquaculture environments.
CNNs have gained prominence in computer vision tasks due to their ability to automatically learn hierarchical representations from images. Unlike traditional neural networks, CNNs use convolutional layers that apply filters to small patches of input data, enabling them to capture local features and spatial hierarchies (Li & Du, 2022), (Rahman et al., 2021). This architecture is particularly effective for image recognition tasks, making CNNs a natural fit for analyzing visual data in aquaculture settings. In the context of automated feed management in aquaculture, CNNs can be employed to process images (Kiran Sre et al., 2008), (Kiran Sre & Ramesh Babu, 2008) or video frames capturing the behavior of aquatic organisms and the distribution of feed within a given environment. The network can learn to recognize patterns associated with healthy feeding behaviors, distinguish between different species, and adapt to varying environmental conditions. This adaptability is crucial for addressing the dynamic nature of aquaculture systems, where factors such as water quality, temperature, and stocking density can influence feeding requirements (Sree, 2008).
CNNs enable real-time visual data processing, enabling continuous monitoring of aquatic organisms and their eating behaviors (Sree et al., 2010). This ensures that feeding schedule adjustments are made quickly in response to observed conditions. By reducing unnecessary feeding, the automated system aims to reduce production costs and lessen its impact on the environment. It achieves this by maximizing feed distribution and taking note of patterns. Aquaculture settings are subject to variations in temperature and water quality (Pokkuluri & Usha, 2021). The CNN-based system can adapt to these differences, ensuring that feeding schedules stay effective under a range of circumstances. The system's cognitive analysis of feeding habits and organism behavior attempts to improve overall health and growth rates by tailoring feed management to the specific needs of each aquatic species (Pokkuluri et al., 2022).