Knowledge discovery and Leaf Spot dynamics of groundnut crop through wireless sensor network and data mining techniques

Tripathy, Amiya Kumar; Adinarayana, J.; Vijayalakshmi, K.; Merchant, S. N.; Desai, U.B.; Ninomiya, S.; Hirafuji, Masayuki; Kiura, Takuji

doi:10.1016/j.compag.2014.05.009

Cited by 35 publications

(18 citation statements)

References 19 publications

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“…Let ℎ denote the throughput. So, formula (2) gives the data selection model based on service opportunistic driven. Here, service request launched the service opportunistic driven in accordance with the probability.…”

Section: Data Selection Mechanism Driven By Mobile Internet Servicesmentioning

confidence: 99%

“…The cloud platform has high performance of data management and efficient data mining [1], which has been widely applied in various fields, such as knowledge discovery and Leaf Spot dynamics [2], and ERP applications [3]. So, the cloud platform [4,5] has played an important role for quality of service guarantee and data mining of mobile Internet.…”

Section: Introductionmentioning

confidence: 99%

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Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

Zhang

2015

Journal of Electrical and Computer Engineering

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Because the static cloud platform cannot satisfy the diversity of mobile Internet service and inefficient data mining problems, we presented a reliable and efficient data mining cloud platform construction scheme based on the mobile Internet service opportunistic driving and application perception. In this scheme, first of all data selection mechanism was established based on mobile Internet service opportunistic drive. Secondly, through the cloud platform different cloud and channel aware, nonlinear mapping from the service to a data set of proposed perceptual model is applied. Finally, on the basis of the driving characteristics and extraction of perceptual features, the cloud platform would be constructed through the service opportunities of mobile Internet applications, which could provide robust and efficient data mining services. The experimental results show that the proposed mechanism, compared to the cloud platform based on distributed data mining, has obvious advantages in system running time, memory usage, and data clustering required time, as well as average clustering quality.

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Section: Data Selection Mechanism Driven By Mobile Internet Servicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

Zhang

2015

Journal of Electrical and Computer Engineering

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“…Site-specific management of crop pests, as well as management of agricultural inputs and irrigation, requires accurate estimates of crop status and agro-meteorological conditions and characterizations of their intra-plot variability [42]. Several authors are unanimous on the fact that management of diseases and pests, characterized by a high spatial and temporal dynamics, requires specific agro-meteorological information at the field and microclimate scales [67][68][69]. Matese et al [70], for example, have shown that the microclimate of vineyards is characterized by high spatial variability (intravignoble and intervignoble) meaning that measurements from meteorological stations located outside of these vineyards do not effectively reflect the microclimate conditions occurring there.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Kotchi¹,

Barrette²,

Viau³

et al. 2016

Geospatial Technology - Environmental and Social Applications

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A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area.

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“…Tripathy et al present knowledge discovery and leaf spot dynamics of groundnut crop by wireless sensor network and data mining techniques. The useful information, knowledge or relations from all kinds of data mining techniques would be helpful to analyze and understand leaf spot disease infection [25]. In order to protect sensor nodes from malicious attacks, Huang et al proposed a new intrusion detection method.…”

Section: Introductionmentioning

confidence: 99%

Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng

Yang

et al. 2016

Applied Sciences

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Abstract:Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

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Knowledge discovery and Leaf Spot dynamics of groundnut crop through wireless sensor network and data mining techniques

Cited by 35 publications

References 19 publications

Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Distributed Global Function Model Finding for Wireless Sensor Network Data

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