Identification and mapping of Individual Trees from Unmanned Aerial Vehicle Imagery Using an object oriented Convolutional Neural Network

Abstract

Abstract Precision agriculture (PA) is an agricultural management strategy founded on the observation, measurement and responding to inter/intra-field crop variability. It includes advances in data collection/analysis and management, along with the technological developments in Data storage and retrieval, accurate positioning, yield monitoring and remote sensing. The last provides unprecedented spatial, spectral, and temporal resolution, but can also provide detailed information about the vegetation's height and various observations. Today, the success of new agricultural technologies has meant that many farming tasks have become automated, and scientists have carried out more studies and companies based on intelligent algorithms that automatically learn decision rules from data. The use of deep learning (DL) and particularly the development and application of some of its algorithms called convolutional neural networks (CNN) is considered a particular success. In the present work, we applied and tested the performance of a simple Convolutional Neural Network to automatically detect and map olive trees from a Phantom4 advanced drone imagery. The workflow involved the image acquisition and the orthomosaic generation with Pix4D software, beside the use of geographical information system and object-based image analyses. The application to a RGB ortho-mosaic of an olive grove, in the east region of Morocco, performed well achieving an overall accuracy of 95.16%.