Precision agriculture can benefit from the us- age of swarms of drones to monitor a field. Crop/weed classification is a concrete applica- tion that can be efficiently carried out through collaborative approaches, whereby the infor- mation gathered by a drone can be exploited as prior to improve the classification per- formed by other drones observing the same area. In this study, we instantiate this con- cept by exploiting state-of-the-art deep learn- ing techniques. We propose the usage of a shallow convolutional neural network that re- ceives as input, besides the RGB channels of the acquired image, also an additional chan- nel that represents a probability map about the presence of weeds in the observed area. Exploiting a realistic, synthetic dataset, the performance is assessed showing a substancial improvement in the classification accuracy.
Using prior information to improve crop/weed classification by MAV swarms
Contributo in atti di convegno
11th INTERNATIONAL MICRO AIR VEHICLE COMPETITION AND CONFERENCE (IMAV 2019), pp. 67–75, 2019
info:cnr-pdr/source/autori:Federico Magistri, Daniele Nardi, Vito Trianni/congresso_nome:11th INTERNATIONAL MICRO AIR VEHICLE COMPETITION AND CONFERENCE (IMAV 2019)/congresso_luogo:/congresso_data:2019/anno:2019/pagina_da:67/pagina_a:75/intervallo_pagine: