This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.

This article reviews the human–machine interaction (HMI) technologies used for telemanipulation by small unmanned systems (SUS) with remote manipulators. SUS, including land, air, and sea vehicles, can perform a wide range of reconnaissance and manipulation tasks with varying levels of autonomy. SUS operations involving physical interactions with the environment require some level of operator involvement, ranging from direct control to goal-oriented supervision. Telemanipulation remains a challenging task for all levels of human interaction because the operator and the vehicle are not colocated, and operators require HMI technologies that facilitate manipulation from a remote location. This article surveys the human operator interfacing for over 70 teleoperated systems, summarizes the effects of physical and visual interface factors on user performance, and discusses these findings in the context of telemanipulating SUS. This article is of importance to SUS researchers and practitioners who will directly benefit from HMI implementations that improve telemanipulation performance.

Automation continues to play a greater role in agricultural production with commercial systems now available for machine vision identification of weeds and other pests, autonomous weed control, and robotic harvesters for fruits and vegetables. The growing availability of autonomous machines in agriculture indicates that there are opportunities to increase automation in cotton production. This article considers how current and future advances in automation has, could, or will impact cotton production practices. The results are organized to follow the cotton production process from land preparation to planting to within season management through harvesting and ginning. For each step, current and potential opportunities to automate processes are discussed. Specific examples include advances in automated weed control and progress made in the use of robotic systems for cotton harvesting.