Segmentation of vegetation and microplots in aerial agriculture images: A survey. Issue 1 (26th May 2022)

• Record Type: Journal Article
• Authors: Mardanisamani, Sara; Eramian, Mark
• Is Part Of: Plant phenome journal. Volume 5:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-26
• Subjects: Phenotype -- Periodicals; Plant genetics -- Periodicals; Periodicals; 581.35
• DOI: 10.1002/ppj2.20042
• Languages: English
• ISSNs: 2578-2703

Abstract

Abstract: Because of the increasing global population, changing climate, and consumer demands for safe, environmentally friendly, and high‐quality food, plant breeders strive for higher yield cultivars by monitoring specific plant phenotypes. Developing new crop cultivars and monitoring through current methods is time‐consuming, sometimes subjective, and based on subsampling of microplots. High‐throughput phenotyping using unmanned aerial vehicle‐acquired aerial orthomosaic images of breeding trials improves and simplifies this labor‐intensive process. To perform per‐microplot phenotype analysis from such imagery, it is necessary to identify and localize individual microplots in the orthomosaics. This paper reviews the key concepts of recent studies and possible future developments regarding vegetation segmentation and microplot segmentation. The studies are presented in two main categories: (a) general vegetation segmentation using vegetation‐index‐based thresholding, learning‐based, and deep‐learning‐based methods; and (b) microplot segmentation based on machine learning and image processing methods. In this study, we performed a literature review to extract the algorithms that have been developed in vegetation and microplots segmentation studies. Based on our search criteria, we retrieved 92 relevant studies from five electronic databases. We investigated these selected studies carefully, summarized the methods, and provided some suggestions for future research. Core Ideas: Algorithms that are commonly used for vegetation segmentation in the field are reviewed. The state‐of‐the‐art algorithms in vegetation segmentation are presented. The state‐of‐the‐art algorithms for microplot segmentation in the field are reviewed. Challenges created by lack of and gaps between plots in microplot segmentation in the field are analyzed. Recommendations are given on algorithms and direction of future research for vegetation and microplot segmentation.