Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies

Abstract: Accurate data acquisition and analysis to obtain crop canopy information are critical steps to understand plant growth dynamics and to assess the potential impacts of biotic or abiotic stresses on plant development. A versatile and easy to use monitoring system will allow researchers and growers to improve the follow-up management strategies within farms once potential problems have been detected. This study reviewed existing remote sensing platforms and relevant information applied to crops and specifically grapevines to equip a simple Unmanned Aerial Vehicle (UAV) using a visible high definition RGB camera. The objective of the proposed Unmanned Aerial System (UAS) was to implement a Digital Surface Model (DSM) in order to obtain accurate information about the affected or missing grapevines that can be attributed to potential biotic or abiotic stress effects. The analysis process started with a three-dimensional (3D) reconstruction from the RGB images collected from grapevines using the UAS and the Structure from Motion (SfM) technique to obtain the DSM applied on a per-plant basis. Then, the DSM was expressed as greyscale images according to the halftone technique to finally extract the information of affected and missing grapevines using computer vision algorithms based on canopy cover measurement and classification. To validate the automated method proposed, each grapevine row was visually inspected within the study area. The inspection was then compared to the digital assessment using the proposed UAS in order to validate calculations of affected and missing grapevines for the whole studied vineyard. Results showed that the percentage of affected and missing grapevines was 9.5% and 7.3%, respectively from the area studied. Therefore, for this specific study, the abiotic stress that affected the experimental vineyard (frost) impacted a total of 16.8 % of plants. This study provided a new method for automatically surveying affected or missing grapevines in the field and an evaluation tool for plant growth conditions, which can be implemented for other uses such as canopy management, irrigation scheduling and other precision agricultural applications.