The paper explores how the number of bird species (target classes) affects the compressibility of neural networks for passive acoustic monitoring on microcontroller units (MCUs). By training and compressing models with varying class counts, the authors show that significant compression can be achieved with minimal performance loss. They also benchmark different hardware platforms and assess the feasibility of deploying energy‑autonomous monitoring devices.
By Nina Brolich, Simon Geis, Maximilian Kasper, Alexander Barnhill, Axel Plinge, Dominik Seu{\ss}
arXiv:2606. 26121v1 Announce Type: cross Abstract: Global insect population declines necessitate scalable, continuous monitoring systems, yet existing vision-based solutions remain constrained by high hardware costs, energy demands, and reliance on centralized processing or cloud connectivity.
By Mattia Consani, Denisa-Andreea Constantinescu, {\AA}se H{\aa}tveit, Titus Venverloo, Fabio Duarte, Carlo Ratti, David Atienza
arXiv:2608.23101v1 Announce Type: cross
Abstract: Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real...
By Nathan Duboisset, Zhaolan Huang, Felix Bie{\ss}mann, Roudy Dagher, Antoine Lavandier, Emmanuel Baccelli
arXiv:2609.22897v1 Announce Type: cross
Abstract: Large vision-language models (VLMs) enable recognition beyond a fixed class set, but their computational demands prevent them from running on many ed...
By Mohammad Mehdi Rastikerdar, Hui Guan, Deepak Ganesan
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
The article outlines a vision and recent progress toward a decentralized system that learns from real‑time building data, treating the building itself as a data center to enhance privacy and reduce dependence on external clouds. It introduces a lightweight, Kubernetes‑like orchestration framework for deploying AI services on low‑power microcontrollers, such as those in the Arduino ecosystem, enabling in‑situ learning on sensors. The work also presents experimental results for new online learning algorithms and proposes a cloud‑fog‑edge architecture using KOptim and FIWARE components.
By Christophe C\'erin, Mamadou Sow, Fr\'ed\'eric Andr\`es
arXiv:2604. 13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management.
By Augustin de la Brosse, Damien Garreau, Thomas Houet, Thomas Corpetti
Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaining paired clear-sky and degraded NDVI data for identical spatiotemporal locations.
arXiv:2607. 09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.
By Linhui Xiao, Guiping Cao, Mingyue Guo, Xianchao Guan, Fan Yang, Ming Tao, Xin Li, Yuxin Peng, Yaowei Wang
arXiv:2606. 27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems.
By Brinnae Bent, Holly R. Houliston, Jiayi Zhou, G\"unel Aghakishiyeva, David W. Johnston
arXiv:2608.29237v1 Announce Type: cross
Abstract: Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances i...
By Malika Nisal Ratnayake, Adel N. Toosi, James Cook, Romina Rader, Alan Dorin
arXiv:2609.13919v1 Announce Type: new
Abstract: This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improvi...
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