arXiv Machine Learning By Fearghal O'Donncha, Nikos Papandroulakis, Jennie Korus, Abigail Langbridge, Alexander Timms, Konstantinos Topouzelis, Abdul Baseer Khan, Shree Rama Kamal Kumar Vegu, Mahtab Sarvmaili, Ryan Mowat, Rhanna Turberville, Tyler Sclodnick, Christopher Whidden

Machine Learning in Fish Farming

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arXiv Machine Learning
2d ago

Enhanced Agriculture-informed Neural Network by Domain Knowledge

The paper introduces KAINN, a hybrid neural‑mechanistic model that augments the Agriculture‑informed Neural Network with domain knowledge on fertilizer diffusion, soil respiration, and water‑filled porosity to predict nitrous oxide emissions from agriculture. Experiments across CNN, LSTM, and Transformer architectures show that KAINN achieves lower root mean square error, lower mean absolute error, and higher R-squared values compared to purely data‑driven models and the original AINN. The learned interfaces exhibit smoother, more physically consistent parameter trajectories with reduced uncertainty.

By Ci Lin, Futong Li, Rose Chong-Wu, Tet Yeap, Iluju Kiringa
arXiv Machine Learning
Jul 28

Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs

arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.

By Dawen Jiang, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Wei Xiang, Jiong Jin
arXiv AI
3d ago

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers

The tutorial titled "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers" explores how modern deep learning techniques—such as neural networks, transformers, large language models, and deep reinforcement learning—can be integrated with operations research and management science to address complex, uncertain, and dynamic decision problems. It argues that deep learning should complement, not replace, optimization, offering adaptability and scalable approximation while OR/MS provides rigorous constraint and uncertainty modeling. The tutorial organizes the field around predict‑then‑optimize, decision‑aware learning, constraint‑aware decision generation, and deep reinforcement learning, and highlights applications across supply chains, healthcare, energy, and autonomous systems.

By I. Esra Buyuktahtakin
arXiv Computer Vision
Sep 11

Does YOLO26 Truly Offer Advantages Over Its Predecessors for Edge Deployment? A Benchmark Study in Aquaculture

The paper evaluates the new YOLO26 architecture, which offers NMS-free end-to-end inference and is tailored for CPU-based edge devices, against three earlier Ultralytics models (YOLOv5u, YOLOv8, and YOLO11) in aquaculture fish mortality detection. Across nano, small, and medium scales, all models achieved similar detection accuracy on a full dataset, but differences emerged in data efficiency and deployment performance: YOLOv8 reached 90% mAP50 with only 400 images, while YOLO26 variants needed 1,000 images; YOLO26n was fastest on a Raspberry Pi 5 (7.51 FPS), whereas YOLOv5mu led on CPU-based hardware. The study concludes that architectural novelty alone does not dictate suitability for edge AI in aquaculture; training data size, target hardware, and inference needs must be jointly considered.

By Rakesh Ranjan, Gajanan S. Kothawade, Kata Sharrer, Scott Tsukuda, Christopher Good