Call for papers: ‘Emerging Bio-robotics’ special section

You are invited to submit a manuscript to be considered for inclusion in an upcoming special section in Agronomy Journal on “Emerging Bio-Robotics for Real-Time Smart Farming.”

Manuscripts should be submitted at https://mc.manuscriptcentral.com/agron by 10 Feb. 2022.

If you have questions on submitting, please contact the Lead Guest Editor, Gunasekaran Manogaran, at Send Message or Send Message.

Overview

With constant population growth, exacerbation of water resources, decreased agricultural land, energy crises, and a decline in the labor force, the agricultural sector faces significant challenges. Moreover, due to rapid industrialization and urbanization, many developed and developing countries face the issue of substantial water crises. Unpredictable climate variation, including intense storms, floods, extreme weather conditions, and heat waves, will adversely impact the agricultural sector. Smart farming with automation can subsequently alleviate the challenges that traditional farming faces. In this regard, agriculture robotics emerge as a promising solution to yield high quality production with minimum expenses and labor in a sustainable way to meet the growing food demands.

Agricultural robots revolutionize the farming industry significantly. The integration of sensing technology, digital tools, and control strategies has enhanced the design and development of agri-robots and demonstrates significant benefits in real-time smart farming. Agri-robot-based smart farming collects weather data and high-resolution field images via ground-based or airborne-based internet of things (IoT) sensors, transmitting these real-time data to a controlling unit that interprets and extracts information for decision-making. The analyzed result is then further sent to field robots, farmers, or agro-industries to take appropriate action. With agri-robots, smart farming faces significant challenges in the context of adaptability, flexibility, object identification, sensor optimization, and task-planning algorithms. To successfully execute the farming task in an autonomous environment, the research should be focused on developing a robotic system with built-in machine intelligence and multifunctionality of the natural organism. To address this challenge, biological functionality with artificial intelligence and machine-learning approaches needs to be introduced to enhance robotic systems performance.