Unsupervised discrimination between lodged and non-lodged winter wheat: a case study using a low-cost unmanned aerial vehicle

Wang, Jianjun; Ge, Hao; Dai, Qigen; Ahmad, Irshad; Dai, Qixing; Zhou, Guisheng; Qin, Mingrong; Gu, Chenghua

doi:10.1080/01431161.2017.1422875

Cited by 26 publications

(12 citation statements)

References 14 publications

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“…Adobe Photoshop [21,100] Applied to correct distortion/use of other image processing methods Agisoft Photoscan [22,36,37] Exploited for the construction of 3D models and orthomosaics. It also allows the calculation of vegetation indices QGIS [23,55] Usually exploited for the calculation of the vegetation indices from multispectral data MATLAB [35,100] Applied mainly for the calculation of vegetation indices.…”

Section: Software Tool Descriptionmentioning

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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Section: Software Tool Descriptionmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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“…Wang et al [22] proposed a method for lodging detection using pixel information obtained from wheat plot images taken by drones. They calculated nine colour features based on pixel values.…”

Section: Related Workmentioning

confidence: 99%

“…We propose a deep convolutional neural network architecture that couples handcrafted and learned features to detect lodging. Previously in the literature, only methods based on handcrafted features have been used for lodging detection from images [14,22,24,25]. Deep convolutional neural networks (DCNNs) have been successfully applied to a wide range of image classification tasks [4,6,7,9,15,19].…”

Section: Introductionmentioning

confidence: 99%

Crop Lodging Prediction From UAV-Acquired Images of Wheat and Canola Using a DCNN Augmented With Handcrafted Texture Features

Mardanisamani

Maleki

Kassani

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the stateof-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at https://github. com/FarhadMaleki/LodgedNet contains code and models.Co-first authors.

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“…As the fundamental method of identifying the feature selection of lodging crops, the index method [7,15,16] is unable to directly determine the optimal number of features. Conversely, the AIC method not only can quickly determine the optimal number of features but also has higher recognition accuracy (Tables 5-7).…”

Section: Generalizability Of the Aic Methods In Selecting Lodging Featuresmentioning

confidence: 99%

“…The number of studies regarding screening methods for lodging identification is limited. The existing methods rely mainly on the difference evaluation index, which has less computational overhead [1,15,16]. However, these methods do not consider the interactions between features, and there is no clear standard for determining the most appropriate feature dimension.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

et al. 2020

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Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments.

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Unsupervised discrimination between lodged and non-lodged winter wheat: a case study using a low-cost unmanned aerial vehicle

Cited by 26 publications

References 14 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

Crop Lodging Prediction From UAV-Acquired Images of Wheat and Canola Using a DCNN Augmented With Handcrafted Texture Features

A Quantitative Monitoring Method for Determining Maize Lodging in Different Growth Stages

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