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

Ulloa, Christyan Cruz; Krus, Anne; Barrientos, Antonio; Cerro, Jaime and Valero, Constantino (2022) Robotic Fertilization in Strip Cropping using a CNN Vegetables Detection-Characterization Method. Computers and Electronics in Agriculture, 193 (106684), pp. 1-13.

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Document available online at: https://www.sciencedirect.com/science/article/pii/S0168169922000011?via%3Dihub


Summary

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.


EPrint Type:Journal paper
Keywords:Organic farming, Strip cropping, ROS, Robotic systems, Convolutional Neural Networks, Deep Learning
Agrovoc keywords:
Language
Value
URI
English
robots
http://aims.fao.org/aos/agrovoc/c_25680
English
vegetables
http://aims.fao.org/aos/agrovoc/c_8174
English
fertilisers -> fertilizers
http://aims.fao.org/aos/agrovoc/c_2867
English
automation
http://aims.fao.org/aos/agrovoc/c_15855
English
intercropping
http://aims.fao.org/aos/agrovoc/c_3910
English
strip cropping
http://aims.fao.org/aos/agrovoc/c_25705
English
neural networks
http://aims.fao.org/aos/agrovoc/c_37467
Subjects: Crop husbandry > Crop combinations and interactions
Crop husbandry > Composting and manuring
Environmental aspects > Air and water emissions
Crop husbandry > Production systems > Vegetables
Farming Systems > Buildings and machinery
Research affiliation: European Union > CORE Organic Cofund > SUREVEG
Spain > CSIC (Spanish National Research Council)
Spain > Polytechnic University of Madrid
ISSN:0168-1699
DOI:10.1016/j.compag.2022.106684
Deposited By: Kristensen, Ph.D. Hanne Lakkenborg
ID Code:43327
Deposited On:19 Jan 2022 07:38
Last Modified:27 Jan 2022 12:19
Document Language:English
Status:Published
Refereed:Peer-reviewed and accepted

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