Firstly, the extraction of the 9 m 2 plots allowed obtaining the following in-situ data: Number of grains per ear and number of ears for each plot. Moreover, the data acquisition was conducted over two phases. in 2018, which was held in China specifically in Beijing and its objective is to estimate the agricultural yield of wheat for 32 varieties, six flight missions that correspond to the following phenological stages: heading, flowering, seed development (Beginning of the stadium milky, soft pasty stage and hard pasty) were made with flight heights between 30 m and 40 m in order to reach spatial resolutions of 2.5 cm to 2.8 cm by the Sequoia camera. Thus, several studies have been conducted to predict wheat yield. It also allows producers who commit to export their crops to plan their actions and decision based on the results of the prediction. In the case of agricultural insurance, yield estimation quantifies the impacts of droughts when they occur to properly determine compensation. Indeed, in order to ensure food security, yield prediction makes it possible to prepare for the consequences of an agricultural shortage, by reducing vulnerability to climatic hazards and to plan in advance aid to farmers and cereal imports. Therefore, the prediction of its yield is a necessity since it is a tool of great interest for decision-making and the basis of measured planning. Wheat is one of the most highly regarded crops for national monitoring because it has been an essential food source for the population for centuries. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development.
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