Fully automated region of interest segmentation pipeline for UAV based RGB images

Abstract

Unmanned Aerial Vehicles (UAVs) have exhibited its potential for efficient and non-invasive crop data acquisition in high throughput crop phenotyping. In general, for analysis of phenotypic traits, there is a need for extracting the region of interest (RoI) from images captured by UAVs. It involves the generation of orthomosaic, which is a complicated and time-intensive process. In this study, a fully automated AI-based pipeline has been proposed for the RoI segmentation from raw RGB images acquired via UAV. The proposed pipeline achieves a near real-time processing speed compared to the other baseline methods. The key feature of the pipeline is the introduction of Sub-Paths , in which the original UAV flight path is divided into several small paths which facilitates parallel processing. The image quality of the extracted RoI has been examined using blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE). The performance of the proposed pipeline is exemplified with the Leaf Area Index (LAI) estimation on five datasets containing three different crop types and growth stages. Regression analysis has also been performed on the estimated LAI values. Average R 2 , RMSE, and correlation scores of the estimates are observed to be 0.68, 0.033, and 0.83, respectively. • Fully automated end-to-end AI-based pipeline avoids using orthomosaic. • Processing time for fixed field and fixed overlap is same, significantly faster than using orthomosaic. • Image quality of the extracted ROI has been analysed using BRISQUE and NIQE indices. • A plant trait has been estimated autonomously as a use case of the proposed framework.