Robotic Fertilization in Strip Cropping using a CNN Vegetables Detection-Characterization Method

Cruz Ulloa, Christyan and Krus, Anne and Barrientos Cruz, Antonio and Cerro Giner, Jaime del and Valero Ubierna, Constantino (2022). Robotic Fertilization in Strip Cropping using a CNN Vegetables Detection-Characterization Method. "Computers and Electronics in Agriculture", v. 193 ; p. 106684. ISSN 0168-1699. https://doi.org/10.1016/j.compag.2022.106684.

Description

Title: Robotic Fertilization in Strip Cropping using a CNN Vegetables Detection-Characterization Method
Author/s:
  • Cruz Ulloa, Christyan
  • Krus, Anne
  • Barrientos Cruz, Antonio
  • Cerro Giner, Jaime del
  • Valero Ubierna, Constantino
Item Type: Article
Título de Revista/Publicación: Computers and Electronics in Agriculture
Date: February 2022
ISSN: 0168-1699
Volume: 193
Subjects:
Freetext Keywords: Organic farming, Strip cropping, ROS, Robotic systems, Convolutional Neural Networks, Deep Learning
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Ingeniería Agroforestal
UPM's Research Group: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA
Creative Commons Licenses: None

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Abstract

To meet the increased demand for organic vegetables and improve their product quality, the Sureveg CORE Organic Cofund ERA-Net project focuses on the benefits and best practices of growing different crops in alternate rows. A prototype of a robotic platform was developed to address the specific needs of this field type at an individual plant level rather than per strip or field section. This work describes a novel method to develop robotic fertilization tasks in crop rows, based on automatic vegetable Detection and Characterization (D.a.C) through an algorithm based on artificial vision and Convolutional Neural Networks (CNN). This network was trained with a data-set acquired from the project’s experimental fields at ETSIAAB-UPM. The data acquisition, processing, anc actuation are carried out in Robot Operating System (ROS). The CNN’s precision, recall, and IoU values as well as characterization errors were evaluated in field trials. Main results show a neural network with an accuracy of 90.5% and low error percentages (<3%) during the vegetable characterization. This method’s main contribution focuses on developing an alternative system for the vegetable D.A.C for individual plant treatments using CNN and low-cost RGB sensors.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainPCI2018‐093074SurevegAntonio BarrientosSUREVEG: Cultivo en franjas y reciclaje de residuos para la producción intensiva y energéticamente eficiente de vegetales (CAR CSIC-UPM)
Government of SpainPCI2018-093046SurevegConstantino ValeroSUREVEG: Cultivo en franjas y reciclaje de residuos para la producción intensiva y energéticamente eficiente de vegetales (ETSIAAB-UPM)

More information

Item ID: 69451
DC Identifier: https://oa.upm.es/69451/
OAI Identifier: oai:oa.upm.es:69451
DOI: 10.1016/j.compag.2022.106684
Official URL: https://www.sciencedirect.com/science/article/pii/S0168169922000011
Deposited by: Profesor Constantino Valero Ubierna
Deposited on: 17 Jan 2022 13:58
Last Modified: 17 Jan 2022 13:58
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