Journal of Mechatronics and Robotics

Developing Next Generation IOT-ML Algorithms in Agriculture & Field Robotics

Description

Global agriculture stands at a critical inflection point. Pressing challenges, climate volatility, persistent labor shortages, and the imperative for sustainable resource use, demand a fundamental shift from traditional practices. To ensure food security and economic resilience, farming must evolve into a precise, automated, and intelligently responsive system.

This Special Issue is dedicated to the algorithmic core of this transformation: the seamless integration of pervasive IoT sensing and edge-based Machine Learning (ML) to create a new generation of intelligent field robotics. We seek research that closes the loop from raw environmental data to immediate robotic action, enabling fully autonomous scouting, precision intervention, and real-time resource management.

Beyond technological innovation, this shift is an economic imperative. In an era of volatile supply chains and rising costs, these algorithms are the key drivers for transitioning to a high-efficiency, data-driven industry. They promise to drastically reduce operational waste, buffer against labor shortages, and maximize sustainable yields, ultimately stabilizing farm economics and the broader food system.

Topics of interest:

We invite submissions on novel algorithms, architectures, and frameworks. Topics include, but are not limited to:

1. Edge-ML for Real-time Robotic Perception: Algorithms for low-latency weed/pest detection and obstacle avoidance directly on field robots.
2. Swarm Intelligence in Field Robotics: Decentralized algorithms for coordinating fleets of small robots or drones for large-scale seeding and harvesting.
3. Generative AI & Digital Twins for Smart Farming: Using LLMs and synthetic data to simulate farm environments and provide natural language decision support to farmers.
4. Energy-Efficient IoT Architectures: Development of battery-free or solar-powered sensors using TinyML for long-term environmental monitoring.
5. Autonomous Navigation in Unstructured Environments: Next-gen SLAM (Simultaneous Localization and Mapping) for robots operating in dusty, muddy, or low-light field conditions.
6. Explainable AI (XAI) for Precision Agriculture: Transparent ML models that allow farmers to understand "why" a specific intervention (like irrigation or fertilization) was recommended.

Guest Editors

Important Dates