Scope

TC 8.1 Control in Agriculture promotes research and development in automatic control, automation, sensing-for-control, modeling, estimation, decision support, optimization, mechatronics, and robotics for agricultural production systems. The committee focuses on methods and applications in which agricultural processes, machines, biological production systems, or farm operations are monitored, modeled, estimated, supervised, optimized, or controlled in order to support automatic, autonomous, or operator-supervised action. Sensing, perception, artificial intelligence, data analysis, and digital technologies are within scope when they contribute to feedback, state or parameter estimation, decision-making, actuation, process coordination, control performance, automation, or autonomous operation. Pure sensing, mapping, image analysis, data analytics, or agronomic assessment without a clear connection to control, automation, supervision, actuation, or actionable operational decision-making is not a primary focus of TC 8.1.

The committee focuses on control and automation problems arising inside agricultural production units, including open fields, field blocks, orchards, vineyards, greenhouses, protected cultivation systems, plant factories, vertical farms, animal houses, agricultural machinery systems, managed forest operation sites, and immediate post-harvest facilities directly connected to agricultural production. In open-field agriculture, the scope is primarily the field, a few adjacent fields, or a farm-level operation, not complete rural regions, catchments, landscapes, or regional environmental systems. In controlled-environment agriculture, the scope concerns the internal operation of the greenhouse, plant factory, vertical farm, or animal house, not regional studies of greenhouse clusters or wider land-use, energy, water, or environmental systems. Forestry-related topics are included when they concern sensing, navigation, machinery, robotics, supervision, or automation within a managed forest stand or operational forest site.

Relevant methodological areas include modeling, system identification, state and parameter estimation, soft sensing, and digital twins for agricultural control and automation; feedback, feedforward, adaptive, robust, optimal, predictive, distributed, and data-driven control of agricultural processes and machines; artificial intelligence, machine learning, and data analytics when used for control, estimation, decision support, autonomy, or operational optimization; sensing, sensor fusion, computer vision, perception, localization, monitoring, and phenotyping when used as part of an automation, control, supervision, or decision-making loop; motion planning, path planning, guidance, navigation, trajectory tracking, and coordination of agricultural machines, robots, implements, and UAVs; mechatronics, actuation, manipulation, implement control, and robotic interaction with plants, animals, soil, crops, and agricultural materials; process monitoring, scheduling, coordination, optimization, and decision support for agricultural operations; communication, IoT, wireless sensor networks, edge/cloud computing, interoperability, and digital infrastructures when supporting agricultural control and automation; and human–machine interaction, supervisory control, safety, reliability, ergonomics, and operator support in agricultural automation.

Representative application domains include autonomous and semi-autonomous agricultural vehicles, implements, machinery, robots, and robotic fleets; crop-care, harvesting, pruning, thinning, spraying, weeding, seeding, scouting, and inspection systems when connected to automation or operational decision-making; field-, greenhouse-, and farm-operation-level irrigation, drainage, fertigation, nutrient application, and water-use control; greenhouse, protected-cultivation, plant-factory, and vertical-farm control of climate, lighting, energy, irrigation, fertigation, crop growth, and production logistics; sensing, monitoring, automation, and environmental control in animal farming systems; precision agriculture, site-specific crop management, and decision support when linked to control actions, prescriptions, machinery operation, or resource optimization; and post-harvest handling, grading, drying, storage, and quality assessment when directly connected to automation, process control, or production-unit operation.

TC 8.1 addresses automation and control challenges that are specific to agricultural and biological production environments, where biological variability, uncertain outdoor or controlled-environment conditions, and interactions among plants, animals, soil, climate, humans, machines, and digital systems create distinctive modeling, estimation, perception, decision-making, and control problems.

The scope excludes general agricultural engineering, agronomy, crop science, remote sensing, mapping, data analytics, or AI studies that do not address control, automation, estimation, supervision, actuation, or operational decision-making. It also excludes general environmental modeling and control at watershed, regional, landscape, or rural-system scale; general biological, physiological, medical, or healthcare systems; general bioprocessing, biotechnology, food engineering, pharmaceutical, wastewater, or industrial-biotechnology processes; and generic robotics, AI, or autonomous-systems research that is not grounded in agricultural production control or automation.