RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing

Sergio Cubero, Ester Marco-Noales, Nuria Aleixos, Silvia Barbé and Jose Blasco
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Sergio Cubero: Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), 7–46113 Valencia, Spain
Ester Marco-Noales: Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), 7–46113 Valencia, Spain
Nuria Aleixos: Departamento de Ingeniería Gráfica, Universitat Politècnica de València (UPV). Camino de Vera, 46022 Valencia, Spain
Silvia Barbé: Centro de Protección Vegetal y Biotecnología, Instituto Valenciano de Investigaciones Agrarias (IVIA), 7–46113 Valencia, Spain
Jose Blasco: Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), 7–46113 Valencia, Spain

Agriculture, 2020, vol. 10, issue 7, 1-13

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 on-board 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.

Keywords: precision agriculture; robotics; computer vision; LiDAR; spectral imaging; precision agriculture; remote sensing; bacterial detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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