RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing
Date
2020Cita bibliográfica
Cubero, Sergio; Marco-Noales, Ester; Aleixos, Nuria; Barbé, Silvia; Blasco, Jose. 2020. RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing. Agriculture 10, no. 7: 276.Abstract
RobHortic is a remote‐controlled field robot that has been developed for inspecting the
presence of pests and diseases in horticultural crops using proximal sensing. The robot is equipped
with colour, multispectral, and hyperspectral (400–1000 nm) cameras, located looking at the ground
(towards the plants). To prevent the negative influence of direct sunlight, the scene was illuminated
by four halogen lamps and protected from natural light using a tarp. A GNSS (Global Navigation
Satellite System) was used to geolocate the images of the field. All sensors were connected to an onboard
industrial computer. The software developed specifically for this application captured the
signal from an encoder, which was connected to the motor, to synchronise the acquisition of the
images with the advance of the robot. Upon receiving the signal, the cameras are triggered, and the
captured images are stored along with the GNSS data. The robot has been developed and tested
over three campaigns in carrot fields for the detection of plants infected with ‘Candidatus
Liberibacter solanacearum’. The first two years were spent creating and tuning the robot and
sensors, and data capture and geolocation were tested. In the third year, tests were carried out to
detect asymptomatic infected plants. As a reference, plants were analysed by molecular analysis
using a specific real‐time Polymerase Chain Reaction (PCR), to determine the presence of the target
bacterium and compare the results with the data obtained by the robot. Both laboratory and field
tests were done. The highest match was obtained using Partial Least Squares‐Discriminant Analysis
PLS‐DA, with a 66.4% detection rate for images obtained in the laboratory and 59.8% for images
obtained in the field.