Towards Image Mosaicking with Aerial Images for Monitoring Rice Crops

Around 8 to 10 million Ton of rice are required in the following years to be able to supply the demand of the overall population. Analysis and monitoring of rice crops becomes nowadays very important issue for farmers, for ensuring a rice production level to cope this demand. This paper presents simulation results of an algorithm that allows to plan and create 2D maps using the technique of image mosaicking with multiple geo-referenced aerial images (multispectral images in the scope of the project). The planning algorithm is called Image Capture algorithm. It takes into account the area the UAV has to cover, the camera configuration, and the state of the UAV in order to define where to take the pictures to build the mosaic. The algorithm presented in this paper was developed in ROS (Indigo) and simulated in Gazebo. The results of this first approach to the 2D mapping of a rice crop allows to see that using the proposed algorithm, it is possible to automate the process of acquiring the pictures for creating the mosaic, ensuring that all the area of interest is covered. By using this algorithm, pictures will be acquired only in specific areas. Therefore, keeping the storage capacity on-board, under control.

[1]  Toshihiro Kujirai,et al.  Mapping Crop Status from AN Unmanned Aerial Vehicle for Precision Agriculture Applications , 2012 .

[2]  Diego Patino,et al.  Multispectral mapping in agriculture: Terrain mosaic using an autonomous quadcopter UAV , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[3]  Richard Szeliski,et al.  Construction of Panoramic Image Mosaics with Global and Local Alignment , 2001 .

[4]  Oskar von Stryk,et al.  Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo , 2012, SIMPAR.

[5]  Deborah Estrin,et al.  Habitat monitoring with sensor networks , 2004, CACM.

[6]  Steven Mills,et al.  Real-time aerial image mosaicing , 2010, 2010 25th International Conference of Image and Vision Computing New Zealand.

[7]  Alessandro Matese,et al.  A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.

[8]  Claudia Arcidiacono,et al.  Classification of crop-shelter coverage by RGB aerial images: a compendium of experiences and findings. , 2010 .

[9]  Gonzalo Pajares,et al.  Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .

[10]  Giorgio C. Buttazzo,et al.  Coverage Path Planning for UAVs Photogrammetry with Energy and Resolution Constraints , 2016, J. Intell. Robotic Syst..

[11]  Sisi Zlatanova,et al.  An automatic mosaicking method for building facade texture mapping using a monocular close-range image sequence , 2010 .

[12]  Massimo Satler,et al.  Towards Smart Farming and Sustainable Agriculture with Drones , 2015, 2015 International Conference on Intelligent Environments.

[13]  Hieu N. Duong,et al.  A Novel Framework Based on Deep Learning and Unmanned Aerial Vehicles to Assess the Quality of Rice Fields , 2016 .

[14]  Xiaoqian Zhang,et al.  Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method , 2015 .

[15]  Naser El-Sheimy,et al.  A FAST APPROACH FOR STITCHING OF AERIAL IMAGES , 2016 .

[16]  R. Y. Hussain.  Production of Digital Mosaics from Aerial Images , 2013 .

[17]  Edward H. Adelson,et al.  A multiresolution spline with application to image mosaics , 1983, TOGS.

[18]  R. Nemani,et al.  Mapping vineyard leaf area with multispectral satellite imagery , 2003 .

[19]  Tatsuhiko Shiraiwa,et al.  Rice yield potential is closely related to crop growth rate during late reproductive period , 2006 .

[20]  Deborah Estrin,et al.  Rapid Deployment with Confidence: Calibration and Fault Detection in Environmental Sensor Networks , 2006 .