Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation Algorithm

Wang, Zhongju; Wang, Long; Zhang, Zijun; Luo, Xiong

doi:10.1109/jiot.2021.3068379

Cited by 16 publications

(15 citation statements)

References 27 publications

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“…For instance, in terms of accuracy as observed from figure 5, DBN RBM [2], CRNS [3], SMAP RF DN [19], GOFCHS [27], TDR [28], and P Band & L Band [34] models outperform other models, thus, they can be used for highly accurate moisture detection applications. Similarly, cost of deployment & computational complexity is visualized from figure 6, wherein it is observed that HPCM [6], HF RFID TFS [9], PWM [10], PMMA [15], FFCSM [16], MHPS [21], ECT [24], PQCWC [25], and HSAAA [32] require lowest deployment cost, while HPCM [6], PHS [17], ECT [24], and PQCWC [25] have lower computational complexity when compared with other models. Due to which these models must be used for applications which require lower complexity and lower computational costs.…”

Section: Discussionmentioning

confidence: 99%

“…This score will allow readers to identify models that have higher accuracy, better scalability, lower complexity, lower cost & lower computational delays. Based on this ARS value, it can be observed that ECT [24], PQCWC [25], MHPS [21], FoS [20], HPCM [6], PHS [17], HF RFID TFS [9], PMMA [15], FFCSM [16], HSAAA [32], PWM [10], FTO [35], CRNS [3] and CM [11] have better overall performance, thus can be used for efficient moisture sensing applications. LEWS LR [1] LR RBM [2] CRNS [3] MWMS [5] GPS [7] HF RFID TFS [9] CM [11] PLMR [12] MSR [14] FFCSM [16] SMAP [18] FoS [20] PRS [22] PQCWC [25] GOFCHS [27] SAR [29] CSMOS [31] SSMDI [33] FTO [35] PBG [38] MSOCCML [40] CRNS [42] Accuracy of moisture sensing models…”

Section: Discussionmentioning

confidence: 99%

“…While highly accurate sensing interfaces are costly, but can be used for high-speed moisture sensing applications. Specifically, DBN RBM, CRNS, SMAP LEWS LR [1] DBN RBM [2] LR RBM [2] BP RBM [2] CRNS [3] FBG [4] MWMS [5] HPCM [6] GPS [7] UHF RFID [8] HF RFID TFS [9] PWM [10] CM [11] PPMR [12] PLMR [12] RFID UHF [13] MSR [14] PMMA [15] FFCSM [16] PHS [17] SMAP [18] SMAP RF DN [19] FoS [20] MHPS [21] PRS [22] ECT [24] PQCWC [25] HDES [26] GOFCHS [27] TDR [28] SAR [29] SMI MODIS [30] CSMOS [31] HSAAA [32] SSMDI [33] P Band & L Band [34] FTO [35] eSMAP [36] PBG [38] MSNs [39] MSOCCML [40] SMAP TFC [41] CRNS [42] kCRNS [43] Computational RF DN, GOFCHS, TDR, and P Band & L Band models outperform other models; thus, they can be used for highly accurate moisture detection applications. While, HPCM, HF RFID TFS, PWM, PMMA, FFCSM, MHPS, ECT, PQCWC, ...…”

Section: Discussionmentioning

confidence: 99%

“…These SAR based methods must be further augmented via use of intelligent sensing interfaces & calibration techniques. Such techniques are discussed in [31,32,33], wherein Calibration of Soil Moisture & Ocean Salinity (CSMOS), hybrid symbolic aggregate approximation algorithm (HSAAA), and satellite-based soil moisture agricultural drought index (SSMDI), are proposed. These models assist in providing high accuracy moisture detection, via augmentation of sensed data, and application of machine learning to estimate & reduce sensing errors.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Ukani

Chakole

2022

J. Phys.: Conf. Ser.

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Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Ukani

Chakole

2022

J. Phys.: Conf. Ser.

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“…Tang et al [26] proposed an improvement in the stochastic optimization of the imaging inverse problems. Recently, the hybrid computational intelligence algorithms were developed and applied in various domains [27][28][29]. Computational intelligence-based algorithms were also employed in the area of image matching.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Rao-NM Algorithm for Image Template Matching

Liu

Wang

et al. 2021

Entropy

Self Cite

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This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.

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A review on smart irrigation management strategies and their effect on water savings and crop yield

Touil

Richa

Fizir

et al. 2022

Irrigation and Drainage

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Appropriate irrigation water management has attracted a considerable degree of attention because it is crucial to guarantee water and food security worldwide. A smart irrigation system is applied for the rational use of water in agricultural fields and for maximizing crop yield. Thus, the high demand for water resources is decreased, and the adverse environmental impacts of irrigation are reduced. Different irrigation approaches were developed to reduce overirrigation by assessing the soil moisture content or crop water stress index. This paper aims to review conventional and smart irrigation technologies and discuss their effect on water savings, yield, and crop quality. The analysis of prominent studies highlighted that the water use efficiency based on soil moisture sensors depends on the volumetric moisture content threshold value set by farmers. Soil moisture sensor controllers, evapotranspiration controllers, and rain sensors have been shown to save 20%-92%, 20%-71%, and 7%-50% of water, respectively, while maintaining crop growth and quality. The optical sensor-based system showed a better ability to assess crop and soil variability in the field than the on-site measurements. The conventional controlled deficit irrigation technique was found to decrease the crop yield to less than 25% with water savings of up to 13%.

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Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation Algorithm

Cited by 16 publications

References 27 publications

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

A Hybrid Rao-NM Algorithm for Image Template Matching

A review on smart irrigation management strategies and their effect on water savings and crop yield

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