Cuscuta spp. Segmentation Based on Unmanned Aerial Vehicles (UAVs) and Orthomasaics Using a U-Net Xception-Style Model

Abstract

Cuscuta spp. is a weed that infests many crops, causing significant losses. Traditional assessment methods and onsite manual measurements are time consuming and labor intensive. The precise identification of Cuscuta spp. offers a promising solution for implementing sustainable farming systems in order to apply appropriate control tactics. This document comprehensively evaluates a Cuscuta spp. segmentation model based on unmanned aerial vehicle (UAV) images and the U-Net architecture to generate orthomaps with infected areas for better decision making. The experiments were carried out on an arbol pepper (Capsicum annuum Linnaeus) crop with four separate missions for three weeks to identify the evolution of weeds. The study involved the performance of different tests with the input image size, which exceeded 70% of the mean intersection-over-union (MIoU). In addition, the proposal outperformed DeepLabV3+ in terms of prediction time and segmentation rate. On the other hand, the high segmentation rates allowed approximate quantifications of the infestation area ranging from 0.5 to 83 m2. The findings of this study show that the U-Net architecture is robust enough to segment pests and have an overview of the crop.