Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Deepa, N.; Khan, Mohammad Zubair; Prabadevi, B.; Vincent, Durai Raj; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy

doi:10.1109/access.2020.3028595

Cited by 47 publications

(12 citation statements)

References 38 publications

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“…The adoption of bespoke processors that perform similar tasks can drastically improve the performance. Finally, because the circuits are tailored for the purpose, energy consumption can be decreased (Deepa et al 2020).…”

Section: Materials Used and Hardware Set-upmentioning

confidence: 99%

RETRACTED ARTICLE: Research on varietal classification and germination evaluation system for rice seed using hand-held devices

Durai

Mahesh

2021

Acta Agriculturae Scandinavica, Section B — Soil & Plant Sc

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Rice Seed varietal classification and germination evaluation system is developed to identify the variety and evaluate the germination of rice seeds using Digital Image processing system. For economic and ease of usage, we have used mobile phones to take digital images. The objective of our research is, it will be easily used by farmers. Our research is done on four major rice varieties, which commonly cultivated by Tamilnadu farmers, namely (1) Andhra Ponni (2) Atchaya Ponni (3) KO50 and (4) IR 20 was collected from Tamilnadu Agricultural University Tiruchirappalli, Tamilnadu, India. We have extracted 24 features: 3 colour features, 13 morphological features and 8 textural features. Created data set tested with all possible classification algorithms, out of which Ensemble classification algorithm gives 91.6% accuracy for Variety Identification and SVM gives 63% of accuracy for germination prediction. According to the germination percentage, a support vector machine (SVM) was utilised to categorise the seeds specimens into 3 groups: healthy, old, and deceased. The categorisation prediction accuracy has always been significant. We have created the data set for successful identification of varieties and germination prediction for the above-mentioned varieties; it is publicly available for usage.

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Section: Materials Used and Hardware Set-upmentioning

confidence: 99%

RETRACTED ARTICLE: Research on varietal classification and germination evaluation system for rice seed using hand-held devices

Durai

Mahesh

2021

Acta Agriculturae Scandinavica, Section B — Soil & Plant Sc

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“…e practice of current traditional centralized security measures may lead us with limitations because of single point of failure, traceability, verifiability, as well as scalability [35]. When we chose multiclass model, development should be done with the consideration of the relative status of the factors taken [36].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

et al. 2022

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Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters.

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“…Proof. For (13) B ∈ n×l , the time complexity of SVD is 4n 2 l + 22L 3 FLOPs through R-SVD [34]. Suppose P ∈ n×l , Q ∈ a×l of GSVD operation.…”

Section: Algorithm Performance Analysismentioning

confidence: 99%

“…Considering that data usually contains information in the form of multivariate, multivariate methods are widely used to capture the relationship between variables. Among them, multivariate statistical process monitoring (MSPM) technology is an effective method for fault detection [3]- [5] in modern industrial processes , can detect abnormal factory data through the model established by off-line training. The common multivariate statistical methods mainly include principal component analysis (PCA) [6], [7], partial least squares (PLS) [8], [9]and independent component analysis (ICA) [10], [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

Li¹,

Yao

et al. 2021

IEEE Access

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To date, quality-related multivariate statistical methods are extensively used in process monitoring and have achieved admirable effects. However, most of them contain recursive processes, which result in higher time complexity and are not suitable for increasingly complex industrial processes. Therefore, this paper embeds singular value decomposition (SVD) into the kernel principal component regression (KPCR) to accomplish Quality-related process monitoring with a lower computational cost. Specifically, the kernel technique is devoted to map the original input into the higher dimensional space to boost the nonlinear ability of the principal component regression (PCR), and then the KPCR is employed to capture the correlation between the input kernel matrix and the output matrix. At the same time, the kernelized input space is decomposed into two orthogonal quality-related and quality-unrelated spaces by SVD, and the statistics of the two spaces are calculated to detect the faults respectively. Compared with other multivariate statistical methods, it has the following advantages: 1) A quality-related kernel principal component analysis (QR-KPCR) algorithm is proposed. 2) Compared with partial least squares method, the recursive process is omitted and the training time is shortened. 3) The model is more concise and the fault detection process is faster. 4) By contrast with other multivariate statistical process monitoring, it has a higher fault detection rate. Experimental results on a widespread example and an industry benchmark verify the effectiveness and reliability of the proposed method.INDEX TERMS Process monitoring, fault detection, kernel principal component regression, multivariate statistical process monitoring.

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Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Cited by 47 publications

References 38 publications

RETRACTED ARTICLE: Research on varietal classification and germination evaluation system for rice seed using hand-held devices

RETRACTED ARTICLE: Research on varietal classification and germination evaluation system for rice seed using hand-held devices

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Quality-Related Process Monitoring Based on Improved Kernel Principal Component Regression

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