Next Article in Journal
Risk Management Tools for Sustainable Agriculture: A Model for Calculating the Average Price for the Season in Revenue Insurance for Citrus Fruit
Next Article in Special Issue
Efficiency of an Integrated Purification System for Pig Slurry Treatment under Mediterranean Climate
Previous Article in Journal
Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV
Previous Article in Special Issue
Treatment of WASTEWATER from the Tannery Industry in a Constructed Wetland Planted with Phragmites australis
Article

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

1
Departamento de Ingeniería Agroforestal, ETSI Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain
2
Farming Systems Ecology Group, Wageningen University & Research, 6700 AK Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 10th Iberian Agroengineering Congress.
Agronomy 2020, 10(2), 197; https://doi.org/10.3390/agronomy10020197
Received: 5 December 2019 / Revised: 15 January 2020 / Accepted: 17 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue Selected Papers from 10th Iberian Agroengineering Congress)
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. View Full-Text
Keywords: lidar; cabbages; weighted sum; point cloud; plant extraction lidar; cabbages; weighted sum; point cloud; plant extraction
Show Figures

Figure 1

MDPI and ACS Style

Krus, A.; van Apeldoorn, D.; Valero, C.; Ramirez, J.J. Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System. Agronomy 2020, 10, 197. https://doi.org/10.3390/agronomy10020197

AMA Style

Krus A, van Apeldoorn D, Valero C, Ramirez JJ. Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System. Agronomy. 2020; 10(2):197. https://doi.org/10.3390/agronomy10020197

Chicago/Turabian Style

Krus, Anne, Dirk van Apeldoorn, Constantino Valero, and Juan J. Ramirez. 2020. "Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System" Agronomy 10, no. 2: 197. https://doi.org/10.3390/agronomy10020197

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop