New Solutions for the Automatic Early Detection of Diseases in Vineyards through Ground Sensing Approaches Integrating LiDAR and Optical Sensors

Abstract

Flavescence dorée and Esca are two of the most important diseases that can affect grapevine. These diseases, if not properly treated in time, are the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Flavescence dorée and Esca is carried out manually through visual surveys usually done by agronomists. These activities require enormous amount of time. Up to now, a solution for a fast and early disease detection of these bacterial and fungal attack was not yet developed. Aim of this research was to test if the use of sensors typically employed in precision agriculture and robotics, mounted on different vehicles, can be a useful tool for crop monitoring purposes, principally for the recognition of disease symptoms. Therefore, two prototypes of a mobile laboratory, the ByeLab (Bionic eye laboratory) and the ATV- LAB, equipped with sensors, were tested on vineyards. The ByeLab is a remote controlled tracked vehicle, able to move between vineyards rows in an easy way also in case of difficult terrain conditions (such as slope or mud). An aluminum frame, specifically designed, was installed on the Byelab. Here, two Lidar sensors (one on the top and the other on the bottom) and six multispectral sensors (three per side, placed at different height) are fixed, respectively on frontal and lateral sides. Thanks to these sensors, the volume of the monitored grapevines and their NDVI were assessed. All the collected values are geo-localized thanks to the presence of a RTK-GNSS receiver, mounted on the top of the frame. Indeed, on the ATV-LAB only two multispectral sensors, one per side and positioned at the same height, and a RTK-GNSS receiver were installed on a support placed frontally. Both vehicles were equipped with a data-logger, in order to store all the collected data. With the aim to identify the grapevine diseases with these two solutions, a visive survey was also conducted to obtain a reference for a comparison. Through this survey, the presence of Flavescence dorée and Esca were assessed and classified according to the stage of the infection. Also the empty spaces along the row, due to missing plants or to the presence of rootstocks, were monitored. From the comparison among manual survey and the assessed NDVI collected by the ByeLab and by the ATV- LAB, the preliminary results show a capability of these systems to detect respectively 80% and 40% of the low vigor condition, as well as diseases and plant absence. From these preliminary results, the ByeLab shows a pretty good capability to identify the presence of low vigor condition along the row.