Reference topic: Innovative biosystems engineering for sustainable agriculture, forestry and food production.
AIIA section: Remote Sensing in agricultural and forestry systems Poster session
Vanessa Lozano1, Giuseppe Brundu1, Luca Ghiani1, Davide Piccirilli1, Albero Sassu2, Maria Teresa Tiloca1, Luigi Ledda1, Filippo Gambella1
1Department of Agriculture, University of Sassari, Viale Italia 39, 07100 Sassari, Italy 2Inspire s.r.l., Via XX settembre 33/10, 16121 Genova, Italy
Detection and monitoring of alien weeds using Unmanned Aerial Vehicle in agricultural systems in Sardinia (Italy)
Emerging technologies such as high-resolution Unmanned Aerial Vehicle (UAV) surveys combined with object-based image analysis and field surveys could represent a reliable, precise and effective tool to support land management in agricultural systems. The technological advances of UAVs can also promote the detection and regular monitoring of invasive alien plants and agricultural weeds. This study was conducted in the framework of two projects funded by the Sardinian Regional Authority, i.e. the project “MARS - Multiple Airdrones Response System” and the project “CarBio - Carciofo Biologico: innovazione e sostenibilità di filiera”. The objective of the study has been to identify, map and monitor alien weed species in agricultural systems to provide an overview on the future applications and challenges of precision farming.
In particular, we evaluated how UAV imagery can be used to map and evaluate the cover of Oxalis pes-caprae, a South African plant species present in a number of crops in Sardinia as an alien invasive weed, with negative direct and indirect effects on the affected crops. Our core assumption is that the most reliable species discrimination can be achieved by targeting flights during flowering (in late winter - early spring) to allow an easier detection due to species-specific spectral differences. To estimate O. pes-caprae cover in the field, we established a network of 1x1 m ground control plots, GPS located, between the artichoke’s rows. Additionally, we assessed the presence of possible correlation between the cover estimated in the field and in the UAV imagery. Therefore, O. pes-caprae infestation was acquired using RGB, Red Edge and NIR cameras installed on board of a Phantom 4 pro (DJI). As a preliminary result, we present the mapping of O. pes-caprae, highlighting the cost-effectiveness and replicability of this approach to detect the presence of this alien weed in agricultural fields.
