Robotic detection and grasp of maize and sorghum: Stem measurement with contact

Abbas Atefi, Yufeng Ge, Santosh Pitla, James Schnable

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Frequent measurements of the plant phenotypes make it possible to monitor plant status during the growing season. Stem diameter is an important proxy for overall plant biomass and health. However, the manual measurement of stem diameter in plants is time consuming, error prone, and laborious. The use of agricultural robots to automatically collect plant phenotypic data for trait measurements can overcome many of the drawbacks of manual phenotyping. The objective of this research was to develop a robotic system that can automatically detect and grasp the stem, and measure its diameter of maize and sorghum plants. The robotic system comprises of a four degree of freedom robotic manipulator, a time-of-flight camera for vision system, and a linear potentiometer sensor to measure the stem diameter. Deep learning and conventional image processing were used to detect stem in images and find grasping point of stem, respectively. An experiment was conducted in a greenhouse using maize and sorghum plants to evaluate the performance of the robotic system. The system demonstrated successful grasping of stem and a high correlation between manual and robotic measurements of diameter depicting its ability to be used as a prototype to integrate other sensors to measure different physiological and chemical attributes of the stem.

Original languageEnglish (US)
Article number58
JournalRobotics
Volume9
Issue number3
DOIs
StatePublished - Sep 2020

Keywords

  • Agricultural robotics
  • Deep learning
  • Faster R-CNN
  • Image processing
  • Plant phenotyping
  • Stem diameter

ASJC Scopus subject areas

  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence

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