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Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

Cruz Ulloa, Christyan; Krus, Anne; Barrientos, Antonio; Del Cerro, Jaime and Valero, Constantino (2020) Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields. Agronomy, 11 (1), p. 11.

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Summary

The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants’ geometric characteristics were extracted from the G-PC as a framework in which the robot’s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%


EPrint Type:Journal paper
Keywords:organic farming; ROS; strip cropping; robotic systems; point cloud localisation; lidar
Agrovoc keywords:
LanguageValueURI
Englishrobotshttp://aims.fao.org/aos/agrovoc/c_25680
Englishstrip croppinghttp://aims.fao.org/aos/agrovoc/c_25705
Englishfertilizationhttp://aims.fao.org/aos/agrovoc/c_2863
Englishsensorshttp://aims.fao.org/aos/agrovoc/c_28279
Subjects: Farming Systems > Buildings and machinery
Farming Systems > Farm nutrient management
Research affiliation: European Union > CORE Organic Cofund > SUREVEG
Spain > CSIC (Spanish National Research Council)
Spain > Polytechnic University of Madrid
ISSN:2073-4395
DOI:10.3390/agronomy11010011
Deposited By: Valero, Constantino
ID Code:38787
Deposited On:15 Jan 2021 13:19
Last Modified:15 Jan 2021 13:19
Document Language:English
Status:Published
Refereed:Peer-reviewed and accepted

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