CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring

Küçük, Çağlar; Kaya, Gulsen Taskin; Erten, Esra

doi:10.1109/igarss.2015.7326737

Cited by 3 publications

(4 citation statements)

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“…The recent research contributions were the main focus of the study though a few important research studies, conducted and investigated in last two decades, were also included. The contributions were reported on various SEM methods used for several purposes, mainly air quality assessment [1,5,11,12,47,58,76,85,89,90]; water pollution monitoring methods [1,13,14,39,64,66,[71][72][73][91][92][93][94][95][96][97]; radiation monitoring methods [1,36]; and smart agriculture monitoring systems [1,14,28,54,60,62,63,[98][99][100][101][102].…”

Section: Discussion Analysis and Recommendationmentioning

confidence: 99%

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

411

109

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Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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Section: Discussion Analysis and Recommendationmentioning

confidence: 99%

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

411

109

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“…Statistical modeling methods are required to develop yield prediction models based on indices deriving from proximal images. Although various models using image data have been constructed to estimate crop yields, these datadriven empirical models differ from the plant variety, climatic region, and the planting location (Gong et al, 2018;Kira & Rendell, 1992;Peng et al, 2019). In this study, we used proximal sensors including RGB and thermal sensors to extract relevant indices such as color, texture, and temperature information for different organs (leaves and spikes) (Figure 1), filtered the features according to different weights, and then utilized machine learning algorithms to predict AGB and grain yield from flowering to mid-late grain filling.…”

Section: Introductionmentioning

confidence: 99%

“…In short, it makes sense to investigate whether feature or dimension reduction helps to search for the most important features and whether the reduced feature set can achieve the same or higher classification accuracy than the original feature set (Lopez-Sanchez et al, 2011). To achieve this goal, feature selection methods are often employed to exclude highly correlated and redundant features from regression analysis by identifying a subset of the original features to maintain meaningful information (Küçük et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

Wang

Zhu

et al. 2022

Food and Energy Security

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Timely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source–sink‐level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High‐quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using proximal sensing technology over a 2‐year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The source dataset and sink dataset performed differently on AGB and GY estimation, but the combined source–sink dataset performed best for estimating both AGB and GY. Based on the source–sink dataset, the LASSO‐RF model was the best combination for predicting AGB and GY, with the coefficient of determination (R2) of 0.85 and 0.86, root mean square error (RMSE) of 1179.09 and 609.61 kg ha−1, and the ratio of performance to deviation (RPD) of 2.10 and 2.45, respectively. This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages.

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“…More importantly, for a given crop type, the methodology is tuned for a specific test area, and therefore, this limits its operational use. An additional drawback of these approaches, recently discussed in [17] and [18], is the difficulty to identify and define phenological intervals to be classified. Some transitions between phenological stages are not well defined in the space defined by the selected features, which, in turn, will lead to poor performance of the estimation (classification) algorithm.…”

mentioning

confidence: 99%

A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

Mascolo

López-Sánchez

Vicente-Guijalba

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Abstract-A new methodology to estimate the growth stages of agricultural crops using the time series of polarimetric synthetic aperture radar (PolSAR) images is proposed. The methodology is based on the complex Wishart classifier and both phenological intervals and training areas are identified measuring the distances among polarimetric covariance matrices obtained from the time series of PolSAR data. Consequently, the computation of PolSAR features, which is the main step of state-of-the-art methods, is no longer needed, and the proposed approach can be applied in the same way to any crop type. Experiments undertaken on a dense time series of fully polarimetric C-band RADARSAT-2 images, collected at incidence angles ranging from 23• to 39• , in ascending/descending orbit passes, demonstrate that the proposed methodology can be successfully applied to retrieve the phenological stages of four different crop types. In addition, the effect of combining beams corresponding to different sensor's configurations has been evaluated, showing that it affects the retrieval accuracies. Validation with ground data shows the following: overall accuracy is between 54% and 86%; producer's accuracy (PA) and user's accuracy (UA) range between 21% and 100% and between 33% and 100%, respectively.

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CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring

Cited by 3 publications

References 10 publications

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

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