A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

Hassanein, Mohamed Sameh; Lari, Zahra; El‐Sheimy, Naser

doi:10.3390/s18041253

Cited by 60 publications

(45 citation statements)

References 35 publications

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“…Examples of sensors used by UAVs for PA: (a) thermal sensor[103]; (b) RGB sensor[59]; (c) multispectral sensor[26]; and (d) hyperspectral sensor[88].…”

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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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“…Examples of sensors used by UAVs for PA: (a) thermal sensor[103]; (b) RGB sensor[59]; (c) multispectral sensor[26]; and (d) hyperspectral sensor[88].…”

mentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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“…A red, green, blue image can be converted to hue-saturation-vibrancy space or grayscale, a color index for precise plant analysis. Additionally, an optimal threshold value can be applied to the converted image to segment plant from background [52][53][54]. Therefore, segmenting vine canopy and minimizing shadow effect can be successfully processed with those applications of RGB camera and Hue histogram conversion [55][56][57][58].…”

Section: Rgb Digital Camerasmentioning

confidence: 99%

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Jang

Kim

et al. 2020

Remote Sensing

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Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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“…The segmentation method is adopted from [33] for the same camera employed as this paper, as it had better performance than vegetation index segmentation using a global threshold [34]. The Hue histogram is extracted after converting the color space of the image from red-green-blue (RGB) to hue-saturation-value (HSV).…”

Section: Ridge Cleaning By Removing Impacts Of Vegetation Coveragementioning

confidence: 99%

Automated Mapping of Typical Cropland Strips in the North China Plain Using Small Unmanned Aircraft Systems (sUAS) Photogrammetry

et al. 2019

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Accurate mapping of agricultural fields is needed for many purposes, including irrigation decisions and cadastral management. This paper is concerned with the automated mapping of cropland strips that are common in the North China Plain. These strips are commonly 3–8 m in width and 50–300 m in length, and are separated by small ridges that assist with irrigation. Conventional surveying methods are labor-intensive and time-consuming for this application, and only limited performance is possible with very high resolution satellite images. Small Unmanned Aircraft System (sUAS) images could provide an alternative approach to ridge detection and strip mapping. This paper presents a novel method for detecting cropland strips, utilizing centimeter spatial resolution imagery captured by sUAS flying at low altitude (60 m). Using digital surface models (DSM) and ortho-rectified imagery from sUAS data, this method extracts candidate ridge locations by surface roughness segmentation in combination with geometric constraints. This method then exploits vegetation removal and morphological operations to refine candidate ridge elements, leading to polyline-based representations of cropland strip boundaries. This procedure has been tested using sUAS data from four typical cropland plots located approximately 60 km west of Jinan, China. The plots contained early winter wheat. The results indicated an ability to detect ridges with comparatively high recall and precision (96.8% and 95.4%, respectively). Cropland strips were extracted with over 98.9% agreement relative to ground truth, with kappa coefficients over 97.4%. To our knowledge, this method is the first to attempt cropland strip mapping using centimeter spatial resolution sUAS images. These results have demonstrated that sUAS mapping is a viable approach for data collection to assist in agricultural land management in the North China Plain.

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A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

Cited by 60 publications

References 35 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Automated Mapping of Typical Cropland Strips in the North China Plain Using Small Unmanned Aircraft Systems (sUAS) Photogrammetry

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