Rebotan

Rebotan is a term used to describe the integration of robotics and botany, focusing on the use of autonomous machines and intelligent systems to study, monitor, and manipulate plant systems. In this framework, robots perform tasks ranging from high-throughput phenotyping and precision irrigation to automated pruning and pollination, often relying on visual sensing, spectral imaging, and environmental sensors.

Researchers and industry practitioners use the term to cover both laboratory plant phenomics and greenhouse automation, as well as field applications such as autonomous crop monitoring and urban farming. The field combines plant science with robotics, computer vision, machine learning, and data analytics to collect data, optimize growth conditions, and increase reproducibility of experiments.

Technologies commonly involved include mobile robot platforms and robotic arms, imaging systems (RGB cameras, multispectral/hyperspectral sensors), LiDAR, actuation systems for manipulation, and AI software for plant recognition, trait extraction, and decision making. Data management, cloud computing, and standardization of protocols are important for cross-study comparability.

Applications span agriculture, horticulture, conservation, and ecological monitoring. Rebotan work aims to reduce labor, improve precision in resource use, accelerate phenotyping pipelines, and enable new experimental designs that would be impractical with manual methods.

Challenges include cost, robustness across outdoor conditions, energy efficiency, data privacy and ownership, and the need for interdisciplinary training. As a developing field, rebotan continues to evolve with advances in sensors, AI, and sustainable farming practices.