Wheat Plots Segmentation for Experimental Agricultural Field from Visible and Multispectral UAV Imaging

Parraga, Adriane; Doering, D.; Atkinson, Joao Gustavo; Bertani, Thiago; Filho, Clódis de Oliveira Andrades; Souza, Mirayr Raul Quadros de; Ruschel, Raphael; Susin, Altamiro

doi:10.1007/978-3-030-01054-6_28

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…All these important plant/crop traits may be potentially detected and determined from single measures in the field, while advances in automated systems might even provide in‐field estimates. The promising results of spectroscopic studies at the organ and canopy levels encourage the up‐scaling of data collection via UAVs, along with the development of image segmentation techniques (Fernandez‐Gallego et al, 2018; Gracia‐Romero et al, 2019; Parraga et al, 2019). Finally, the development and integration of computing systems and advanced statistics (e.g., spectral variable selection, data reduction, machine learning systems, and ensemble methods; see “Phenotyping Platforms” section) as a key part of the in‐plant phenotyping pipeline has contributed to the translation of huge amounts of spectral information into useful phenotypic data for breeders, molecular biologists, and ecophysiologists.…”

Section: Remote Sensingmentioning

confidence: 99%

Crop phenotyping in a context of global change: What to measure and how to do it

Araus

Kefauver

Vergara-Díaz

et al. 2022

JIPB

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High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/ methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies.

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Section: Remote Sensingmentioning

confidence: 99%

Crop phenotyping in a context of global change: What to measure and how to do it

Araus

Kefauver

Vergara-Díaz

et al. 2022

JIPB

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“…The visible channel that is no longer captured by the modified RGB sensor is often captured by using another embedded RGB sensor. The use of both multispectral and visible sensors was observed in many cases [22,23,27,31,33,49,50,55,56,61,65,69,72,87,103,107,113,117].…”

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confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…Automatic methods do not require manual inputs or user intervention. Parraga et al (2018) proposed a segmentation method based on image processing techniques using UAV imagery. There are four steps, preprocessing, filtering, ROI map, and validation.…”

Section: Automatic Methodsmentioning

confidence: 99%

Segmentation of vegetation and microplots in aerial agriculture images: A survey

Mardanisamani

Eramian

2022

The Plant Phenome Journal

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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.

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Wheat Plots Segmentation for Experimental Agricultural Field from Visible and Multispectral UAV Imaging

Cited by 9 publications

References 13 publications

Crop phenotyping in a context of global change: What to measure and how to do it

Crop phenotyping in a context of global change: What to measure and how to do it

A Review on UAV-Based Applications for Precision Agriculture

Segmentation of vegetation and microplots in aerial agriculture images: A survey

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