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Abstract
To help address the global growing demand for food and fiber, selective breeding programs aim to cultivate crops with higher yields and more resistance to stress. Measuring phenotypic traits needed for breeding programs is usually done manually and is labor-intensive, subjective, and lacks adequate temporal resolution. This paper presents a Multipurpose Autonomous Robot of Intelligent Agriculture (MARIA), an open source differential drive robot that is able to navigate autonomously indoors and outdoors while conducting plant morphological trait phenotyping and soil sensing. For the design of the rover, a drive system was developed using the Robot Operating System (ROS), which allows for autonomous navigation using Global Navigation Satellite Systems (GNSS). For phenotyping, the robot was fitted with an actuated LiDAR unit and a depth camera that can estimate morphological traits of plants such as volume and height. A three degree-of-freedom manipulator mounted on the mobile platform was designed using Dynamixel servos that can perform soil sensing and sampling using off-the-shelf and 3D printed components. MARIA was able to navigate both indoors and outdoors with an RMSE of 0.0156 m and 0.2692 m, respectively. Additionally, the onboard actuated LiDAR sensor was able to estimate plant volume and height with an average error of 1.76% and 3.2%, respectively. The manipulator performance tests on soil sensing was also satisfactory. This paper presents a design for a differential drive mobile robot built from off-the-shelf components that makes it replicable and available for implementation by other researchers. The validation of this system suggests that it may be a valuable solution to address the phenotyping bottleneck by providing a system capable of navigating through crop rows or a greenhouse while conducting phenotyping and soil measurements.
Highlights
The growing global human population demands more food and fiber supplies which are threatened by the changing climate and less arable land [1]
This paper presents a design for a differential drive mobile robot built from off-the-shelf components that makes it replicable and available for implementation by other researchers
To achieve the full potential of modern genomics and breeding programs, the phenotypic traits—the physical and bio-chemical characteristics—of crops need to be measured so these traits can be linked with promising genetic locations, such as quantitative trait loci (QTL)
Summary
The growing global human population demands more food and fiber supplies which are threatened by the changing climate and less arable land [1]. To achieve the full potential of modern genomics and breeding programs, the phenotypic traits—the physical and bio-chemical characteristics—of crops need to be measured so these traits can be linked with promising genetic locations, such as quantitative trait loci (QTL). These phenotypic traits have been measured manually, which is labor intensive, inefficient, and subjective. Electronics 2020, 9, 1550 constraints, plant breeders are not able to measure crop traits throughout the growing season and only at the end of the season. The phenotypic data with such a limited temporal resolution cannot help elucidate complex genetic basis of certain traits such as yield and flowering time [3]
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