Robotic Detection and Grasp of Maize and Sorghum: Stem Measurement with Contact

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.

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

“…The robot measured the reflectance spectra, temperature, and fluorescence by imaging the leaf or placing probes with millimeter distance from the leaf surface (Figure 1B). Two different plant phenotyping robotic systems were introduced to measure leaf and stem properties of maize and sorghum plants (Atefi et al, 2019(Atefi et al, , 2020. The FIGURE 1 | Plant phenotyping robotic systems for indoor environment: (A) A multi-robot system equipped with deep learning technique to determine optimal viewpoints for 3D model reconstruction (Wu et al, 2019), (B) Sensor-equipped robot to measure the reflectance spectra, temperature, and fluorescence of leaf (Bao et al, 2019c), (C) Robotic system to measure leaf reflectance and leaf temperate (Atefi et al, 2019), and (D) Robotic system for direct measurement of leaf chlorophyll concentrations (Alenyá et al, 2014).…”

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

“…Conclusively, contact measurement techniques are also viable for determining the diameter of maize stems. Atefi et al. (2020) utilized a robotic system fitted with fixtures to measure the diameters of maize and sorghum stems under controlled laboratory conditions, yielding R ² values of 0.98 and 0.99, respectively.…”

Robotics and Autonomous Systems for Net Zero Agriculture

“…Most physiological and vegetative indices are purely spectral-based derivations while robotic platforms for measuring cellular traits under field conditions are currently unavailable. Such robotic systems need viable designs such as specialised and sensible robotic arms to attach sensors and to hold plant organs [41] and need precise deployment of vision-guided segmentation of specific plant organs [42]. Field-viable mobile robotic platforms for large-scale phenotyping are scarce and any such existing robotic phenotyping platforms have temporal limitations due to energy demands [6], sensor usage restrictions [43], or unable to cope with unstructured and harsh field environments.…”