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.
Highlights
Plant phenotyping is a key technology for the plant breeders to produce crops with desirable traits such as higher yield, disease resistance, and drought tolerance [1]
CNN, image processing to determine the grasping point on the stem, inverse kinematics to calculate the joint angles of the robotic manipulator, stem grasping, and sensing inverse kinematics calculate the joint angles of the robotic stem grasping, andtimes sensing process to measureto stem diameter using
An experiment was conducted in the greenhouse using maize and sorghum plants to compare the robotic and ground-truth measurements of stem diameter and evaluate the performance of the system
Summary
Plant phenotyping is a key technology for the plant breeders to produce crops with desirable traits such as higher yield, disease resistance, and drought tolerance [1]. Plant phenotyping typically assesses quantitatively the crop phenotypes such as leaf area, leaf angle, and stem diameter [2]. The stem diameter of a plant is a good indicator of plant’s health and biomass accumulation. A plant with a thicker stem is usually considered as healthier with higher yield potential than other plants at the same growth stage [3]. The manual measurement of stem diameter is laborious, time consuming, and error prone [4]. Robotic phenotyping will help plant geneticists to investigate the interaction between genotype and environment more readily in order to improve crop yields and resilience to environmental stresses [1]. Literature reports many image-based stationary phenotyping systems to mitigate the issues with manual measurements
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