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Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System

Krus, Anne; van Apeldoorn, Dirk; Valero, Constantino and Ramirez, Juan José (2020) Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System. Agronomy, 10 (2), p. 197.

[thumbnail of Krus et al. - 2020 - Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System.pdf] PDF - Published Version - English
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Document available online at: https://www.mdpi.com/2073-4395/10/2/197


The SUREVEG project focuses on improvement of biodiversity and soil fertility in organic agriculture through strip-cropping systems. To counter the additional workforce a robotic tool is proposed. Within the project, a modular proof of concept (POC) version will be produced that will combine detection technologies with actuation on a single-plant level in the form of a robotic arm. This article focuses on the detection of crop characteristics through point clouds obtained with two lidars. Segregation in soil and plants was successfully achieved without the use of additional data from other sensor types, by calculating weighted sums, resulting in a dynamically obtained threshold criterion. This method was able to extract the vegetation from the point cloud in strips with varying vegetation coverage and sizes. The resulting vegetation clouds were compared to drone imagery, to prove they perfectly matched all green areas in said image. By dividing the remaining clouds of overlapping plants by means of the nominal planting distance, the number of plants, their volumes, and thereby the expected yields per row could be determined.

EPrint Type:Journal paper
Keywords:Cabbages; Lidar; Plant extraction; Point cloud; Weighted sum
Agrovoc keywords:
cabbage (plant) -> Brassica oleracea capitata
detectors -> sensors
Subjects: Farming Systems > Buildings and machinery
Research affiliation: European Union > CORE Organic > CORE Organic Cofund > SUREVEG
Deposited By: Krus, A.M.
ID Code:38517
Deposited On:27 Oct 2020 15:39
Last Modified:27 Oct 2020 15:39
Document Language:English
Refereed:Peer-reviewed and accepted

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