1. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management
Remote sensing can provide a fast and reliable alternative for traditional in situ water status measurement in vineyards. Several vegetation indices (VIs) derived from aerial multispectral imagery were tested to estimate midday stem water potential (Ψstem) of grapevines. The experimental trial was carried out in a vineyard in the Shangri-La region, located in Yunnan province in China. Statistical methods and machine learning algorithms were used to evaluate the correlations between Ψstem and VIs. Results by simple regression between VIs individually and Ψstem showed no significant relationships, with coefficient of determination (R2) for linear fitting smaller than 0.3 for almost all the indices studied, except for the Optimal Soil Adjusted Vegetation Index (OSAVI); R2 = 0.42 with statistical significance (p ≤ 0.001). However, results from a model obtained by fitting using Artificial Neural Network (ANN), using all VIs calculated as inputs and real Ψstem from plants within the study site (n = 90) as targets (Model 1), showed high correlation between the estimated water potential through ANN (Ψstem ANN) and the actual measured Ψstem. Training, validation and testing data sets presented individual correlations of R = 0.8, 0.72 and 0.62 respectively. The models obtained from the study site were then applied to a wider area from the vineyard studied and compared to further Ψstem measured obtained from different sites (n = 23) showing high correlation values between Ψstem ANN and real Ψstem (R2 = 0.83; slope = 1; p ≤ 0.001). Finally, a pattern recognition ANN model (Model 2) was developed for irrigation scheduling purposes using the same Ψstem measured in the study site as inputs and with the following thresholds as outputs: Ψstem below −1.2 MPa considered as severe water stress (SS), Ψstembetween −0.8 to −1.2 MPa as moderate stress (MS) and Ψstem over −0.8 MPa with no water stress (NS). This model can be applied to analyze on a plant by plant basis to identify sectors of stress within the vineyard for optimal irrigation management and to identify spatial variability within the vineyards.
2. Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies
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.