TY - JOUR
T1 - AI-based autonomous UAV swarm system for weed detection and treatment
T2 - enhancing organic orange orchard efficiency with agriculture 5.0
AU - Catala-Roman, Paula
AU - Segura-Garcia, Jaume
AU - Smith, Esther
AU - Navarro-Camba, Enrique A.
AU - Alcaraz-Calero, Jose M.
AU - Garcia-Pineda, Miguel
PY - 2024/12/31
Y1 - 2024/12/31
N2 - Weeds significantly threaten agricultural productivity by competing with crops for nutrients, particularly in organic farming, where chemical herbicides are prohibited. On Spain’s Mediterranean coast, organic citrus farms face increasing challenges from invasive species like Araujia sericifera and Cortaderia selloana, which further complicate cover crop management. This study introduces a swarm system of unmanned aerial vehicles (UAVs) equipped with neural networks based on YOLOv10 for the detection and geo-location of these invasive weeds. The system achieves F1-scores of 0.78 for Araujia sericifera and 0.80 for Cortaderia selloana. Using GPS and RTK, the UAVs generate KML files to guide diffuser drones for precise, localized treatments with organic products. By automating the detection, treatment, and elimination of invasive species, the system enhances both productivity and sustainability in organic farming. Additionally, the proposed solution addresses the high labor costs associated with manual weeding by reducing the need for human intervention. A comprehensive economic analysis indicates potential savings ranging from 1810 to 2650 € per hectare, depending on farm size. This innovative approach not only improves weed control efficiency but also promotes environmental sustainability, offering a scalable solution for organic and conventional agriculture alike.
AB - Weeds significantly threaten agricultural productivity by competing with crops for nutrients, particularly in organic farming, where chemical herbicides are prohibited. On Spain’s Mediterranean coast, organic citrus farms face increasing challenges from invasive species like Araujia sericifera and Cortaderia selloana, which further complicate cover crop management. This study introduces a swarm system of unmanned aerial vehicles (UAVs) equipped with neural networks based on YOLOv10 for the detection and geo-location of these invasive weeds. The system achieves F1-scores of 0.78 for Araujia sericifera and 0.80 for Cortaderia selloana. Using GPS and RTK, the UAVs generate KML files to guide diffuser drones for precise, localized treatments with organic products. By automating the detection, treatment, and elimination of invasive species, the system enhances both productivity and sustainability in organic farming. Additionally, the proposed solution addresses the high labor costs associated with manual weeding by reducing the need for human intervention. A comprehensive economic analysis indicates potential savings ranging from 1810 to 2650 € per hectare, depending on farm size. This innovative approach not only improves weed control efficiency but also promotes environmental sustainability, offering a scalable solution for organic and conventional agriculture alike.
KW - AI-based weed recognition
KW - UAV
KW - geopositioning
KW - autonomous swarm system
KW - digital-farming
KW - Agriculture 5.0
U2 - 10.1016/j.iot.2024.101418
DO - 10.1016/j.iot.2024.101418
M3 - Article
SN - 2542-6605
VL - 28
JO - Internet of Things
JF - Internet of Things
M1 - 101418
ER -