Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.
Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
In vivo Human-Like Robotic Phenotyping of Leaf and Stem Traits in Maize and Sorghum in Greenhouse
Abstract
In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, tedious, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, which enables automatic monitoring with minimal human intervention. In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare to manual measurements. The robotic systems comprised of a four degree of freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. Robotic grippers were developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, a thermistor for leaf temperature measurement, and a linear potentiometer for stem diameter measurement. An Image processing technique and deep learning method were used to identify grasping points on leaves and stems, respectively. The systems were tested in a greenhouse using maize and sorghum plants. The results from the leaf phenotyping robot experiment showed that leaf temperature measurements by the phenotyping robot were correlated with those measured manually by a human researcher (R2 = 0.58 for maize and 0.63 for sorghum).The leaf spectral measurements by the phenotyping robot predicted leaf chlorophyll, water content and potassium with moderate success (R2 ranged from 0.52 to 0.61), whereas the prediction for leaf nitrogen and phosphorus were poor. The total execution time to grasp and take measurements from one leaf was 35.5±4.4 s for maize and 38.5±5.7 s for sorghum. Furthermore, the test showed that the grasping success rate was 78% for maize and 48% for sorghum. The experimental results from the stem phenotyping robot demonstrated a high correlation between the manual and automated stem diameter measurements (R2 > 0.98). The execution time for stem diameter measurement was 45.3 s. The system could successfully detect and localize, and also grasp the stem for all plants during the experiment. Both robots could decrease the tediousness of collecting phenotypes compare to manual measurements. The phenotyping robots can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo morphological, physiological, and biochemical trait measurements for plant leaves and stems.
Subject Area
Agricultural engineering|Agriculture
Recommended Citation
Atefi, Abbas, "In vivo Human-Like Robotic Phenotyping of Leaf and Stem Traits in Maize and Sorghum in Greenhouse" (2019). ETD collection for University of Nebraska-Lincoln. AAI22591987.
https://digitalcommons.unl.edu/dissertations/AAI22591987