WITHDRAWN: Full smart sprinklers: Monitoring of sprinkler watering using IoT

Santhi, M.; Raju, S. Hrushikesava; Krishna, Pravin; Koujalagi, Ashok

doi:10.1016/j.matpr.2020.12.399

Cited by 1 publication

(3 citation statements)

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“…From the above equation ( 9), 'Stand(β)' forms the standardized criterion and the negligible classification function that includes irrigation variables (i.e., irrigation variable is set to 1 when the irrigation is turned on and the irrigation variable is set to 0 when the irrigation is turned off) associated with each of the four fields (i.e., maize [less water irrigation], peanuts [irrigation based on water loss], peanuts [less water irrigation] and peanuts [normal irrigation]) using Bernoulli distribution is mathematically formulated as given below. (10) Then, the final soil moisture predicted value using the soil humidity is formulated according to the hidden layer (i.e., mining and classification results) and visual layer (i.e., soil humidity) functional values. The activation probability of 'jth' element with known 'vl' units is formulated as given below.…”

Section: (9)mentioning

confidence: 99%

“…The pseudo code representation of deep Bernoulli and Boltzmann IoT-based soil quality prediction is given below. Step 4: Formulate pair of Boolean vectors for hidden and visual layer vectors as in equation (8) Step 5: Estimate hinge probability distribution as in equation (9) Step 6: Evaluate logistic function to visible unit based on Bernoulli distribution as in equation (10) Step 7: Formulate activation probability of 'jth' element with known 'vl' units as in equation (11) Step 8: Formulate activation probability of 'ith' element with known 'hl' units as in equation ( 12 As given in the deep Bernoulli and Boltzmann IoT-based soil quality prediction algorithm for soil quality prediction, with the objective of improving the sensitivity and specificity rate involved in the classification process, a duality problem is formulated. First with the relevant feature selected as input, a pair of Boolean vectors is modeled for predicting soil quality.…”

Section: (11)mentioning

confidence: 99%

“…The monitoring of sprinklers in a continuous manner is necessitated by means of surveillance with the purpose of recognizing drawbacks but still necessitates human intervention to eliminate certain issues. Therefore, to eliminate continuous monitoring and also to minimizing the human efforts a novel method utilizing Internet of things (IoT) by means of proximity sensor, temperature of soil sensor, on the basis of the water flow and land wetness were proposed in [10].…”

Section: Introductionmentioning

confidence: 99%

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Big Data Application in the Neyman-Pearson Regression and Deep Bernoulli and Boltzmann for Iot Based Soil Quality Prediction

Balaji¹,

Vijayakumar²

2022

SJLSA

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Purpose. The aim of the work is to enhance the soil quality prediction accuracy and time by using feature selection and classification-based model. Background. Soil quality analysis was handled based on the farmer’s first-hand data mining competence and with the world population expected to increase exponentially (i.e., big data), erratic changes in climate have started influencing soil capitulates incorrectly. Big data is employed used to examine the large amount of dataset for soil quality analysis. It is helps to address a lot of new and significant farming decisions and issue. Soil quality analysis depends on fertility of the soil. By soil quality analysis accuracy prediction is very crucial for practical utilization of resources. But, the existing data mining techniques failed to select the correct crop based on the soil and environmental features. Material and methods. The study presents the data mining techniques based on smart and efficient soil quality prediction for agriculture development. In our work, first, linear regression and the Neyman-Pearson correlation-based feature selection model is employed to obtain the computationally efficient and relevant features. Next, an enhanced deep learning model called deep Bernoulli and Boltzmann IoT-based soil quality prediction is designed to classify the complex soil features with better sensitivity and specificity. Results. Experimental results obtained confirmed the performance and reliability of the proposed method. The result evaluations are carried out on the basis of the prediction accuracy, prediction time, sensitivity and specificity. Conclusion. The result shows that the NPR-DBB method achieves better results than the state-of-the-art methods.

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Section: (9)mentioning

confidence: 99%