TY - JOUR
T1 - In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse
AU - Atefi, Abbas
AU - Ge, Yufeng
AU - Pitla, Santosh
AU - Schnable, James
N1 - Funding Information:
This work was supported by USDA-NIFA, United States (Award# 2017-67007-25941 ). The authors would like to thank Greenhouse staff Vincent Stoerger and Troy Pabst for their assistance in experiment design and plant caring, and undergraduate student Ema Muslic for her assistance in data collection.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/8
Y1 - 2019/8
N2 - In plant phenotyping, leaf-level physiological and chemical trait measurements are needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, enabling automated monitoring with minimal human intervention. In this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties. The robotic system comprised of a four Degree of Freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. A robotic gripper was developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, and a thermistor for leaf temperature measurement. A MATLAB program along with a Graphical User Interface (GUI) was developed to control the robotic system and its components, and for acquiring and recording data obtained from the sensors. The system was tested in a greenhouse using maize and sorghum plants. The results showed that leaf temperature measurements by the phenotyping robot were significantly 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 phenotyping robot can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo leaf-level physiological and biochemical trait measurements.
AB - In plant phenotyping, leaf-level physiological and chemical trait measurements are needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, enabling automated monitoring with minimal human intervention. In this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties. The robotic system comprised of a four Degree of Freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. A robotic gripper was developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, and a thermistor for leaf temperature measurement. A MATLAB program along with a Graphical User Interface (GUI) was developed to control the robotic system and its components, and for acquiring and recording data obtained from the sensors. The system was tested in a greenhouse using maize and sorghum plants. The results showed that leaf temperature measurements by the phenotyping robot were significantly 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 phenotyping robot can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo leaf-level physiological and biochemical trait measurements.
KW - Agricultural robotics
KW - Image processing
KW - Leaf reflectance
KW - Leaf temperature
KW - Machine vision
KW - Plant phenotyping
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U2 - 10.1016/j.compag.2019.104854
DO - 10.1016/j.compag.2019.104854
M3 - Article
AN - SCOPUS:85067170229
VL - 163
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
SN - 0168-1699
M1 - 104854
ER -