Deep learning for smart fish farming: applications, opportunities and challenges
Beijing Research Center for Information Technology in Agriculture（北京农业信息技术研究中心）；National Engineering Research Center for Information Technology in Agriculture（国家农业信息技术工程研究中心）；National Engineering Laboratory for Agri-product Quality Traceability（农产品质量溯源国家工程实验室）；Ocean University of China（中国海洋大学）；Tianjin University of Science and Technology（天津科技大学）；University of Almeria（阿尔梅里亚大学）
With the rapid emergence of deep learning (DL) technology, it has been successfully used in various fields including aquaculture. This change can create new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on the applications of DL in aquaculture, including live fish identification, species classification, behavioral analysis, feeding decision-making, size or biomass estimation, water quality prediction. In addition, the technical details of DL methods applied to smart fish farming are also analyzed, including data, algorithms, computing power, and performance. The results of this review show that the most significant contribution of DL is the ability to automatically extract features. However, challenges still exist; DL is still in an era of weak artificial intelligence. A large number of labeled data are needed for training, which has become a bottleneck restricting further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs in the handling of complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for the implementation of smart fish farming.
Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants. In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded Convolutional Neural Network (CNN), Segnet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed da…
Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in deep learning techniques have further enhanced the precision and reliability of aerial imagery based analysis. The capabilities to mount various kinds of sensors (RGB, spectral cameras) on UAV allows remote crop analysis applications such as vegetation classification and segmentation, crop counting, yield monitoring and prediction, crop mapping, weed detection, disease and nut…
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset
College of Information Engineering, Sichuan Agricultural University（四川农业大学信息工程学院）；College of Mechanical and Electrical Engineering, Sichuan Agricultural University（四川农业大学机电工程学院）；Ya’an Digital Agricultural Engineering Technology Research Center（雅安数字农业工程技术研究中心）
FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture
Dalian Key Laboratory of Smart Fishery（大连智能渔业重点实验室）；College of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；Key Laboratory of Environment Controlled Aquaculture（环境可控养殖重点实验室）；Dalian Polytechnic University School of International Education（大连职业技术学院国际教育学院）
Cross-Camera Cow Identification via Disentangled Representation Learning
College of Information Science and Engineering, Shandong Agricultural University（信息科学与工程学院，山东农业大学）；Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey（视觉语音与信号处理中心（CVSSP）， Surrey 大学）；Australian Institute for Machine Learning, University of Adelaide（机器学习研究院，阿德莱德大学）
Smart weeding systems to perform plant-specific operations can contribute to the sustainability of agriculture and the environment. Despite monumental advances in autonomous robotic technologies for precision weed management in recent years, work on under-canopy weeding in fields is yet to be realized. A prerequisite of such systems is reliable detection and classification of weeds to avoid mistakenly spraying and, thus, damaging the surrounding plants. Real-time multi-class weed identification enables species-specific treatment of weeds and significantly reduces the amount of herbicide use. H…
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
MBZ University of AI（人工智能大学）；CECS, Australian National University（计算机科学与工程系，澳大利亚国立大学）；Computer Vision Laboratory, Linköping University（链接öping大学计算机视觉实验室）
FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking
National Innovation Center for Digital Fishery（国家数字渔业创新中心）；Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs（农业农村部水产动物与畜禽智慧养殖技术重点实验室）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；College of Engineering, China Agricultural University（中国农业大学工学院）；Smart City College, Beijing Union University（北京联合大学智慧城市学院）
A Comprehensive Review of Fish Feeding Behavior Analysis in Aquaculture: Tasks, Techniques, and Applications
National Innovation Center for Digital Fishery（数字渔业国家创新中心）；Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock（水产动植物智能养殖技术重点实验室）；State Key Laboratory of Efficient Utilization of Agricultural Water Resources（农业用水高效利用国家重点实验室）；Beijing Engineering and Technology Research Center for Internet of Things in Agriculture（农业物联网工程技术研究中心）；Key Laboratory of Digital Fisheries of Shandong Province（山东省数字渔业重点实验室）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）
Progressive Multimodal Interaction Network for Reliable Quantification of Fish Feeding Intensity in Aquaculture
National Innovation Center for Digital Fishery（国家数字渔业创新中心）；Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs（农业农村部水产动物与畜禽智慧养殖技术重点实验室）；State Key Laboratory of Efficient Utilization of Agricultural Water Resources（农业水资源高效利用全国重点实验室）；Beijing Engineering and Technology Research Center for Internet of Things in Agriculture（北京市农业物联网工程技术研究中心）；Key Laboratory of Digital Fisheries of Shandong Province（山东省数字渔业重点实验室）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）
From operculum and body tail movements to different coupling of physical activity and respiratory frequency in farmed gilthead sea bream and European sea bass. Insights on aquaculture biosensing
Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction
Dom Bosco Catholic University（多姆博斯科天主教大学）；Federal University of Mato Grosso do Sul（南马托格罗索联邦大学）；Pantanal Biopark（潘塔纳尔生物公园）；State University of Mato Grosso do Sul（南马托格罗索州立大学）；Kerow Precision Solutions（Kerow精密解决方案公司）
A Deep Learning-based Pest Insect Monitoring System for Ultra-low Power Pocket-sized Drones
Dalle Molle Institute for Artificial Intelligence (IDSIA)（达勒·莫勒人工智能研究所）；USI（瑞士意大利语区大学）；SUPSI（瑞士应用科学大学）；Integrated Systems Laboratory (IIS)（集成系统实验室）；ETH Zürich（苏黎世联邦理工学院）
