e-PADI: an iot-based paddy productivity monitoring and advisory system

Ismail, Muhammad Aiman; Isa, Mohd Nazrin Md; Mohyar, S. N.; Ahmad, Megat Harun Al Rashid Megat; Ismail, Md Yusof; Ismail, Rizalafande Che; Harun, A.; Murad, S. A. Z.

doi:10.11591/ijeecs.v14.i2.pp852-858

Cited by 12 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The individual model exhibits its own pros and cons and is best fit for specific types of target complications. A discussion of problems in the Internet of Things (IoT) application [5][6][7][8][9] using regression analysis has been presented in [10]. In addition, each model can be recyclable and is built by a training set of algorithms using classical data.…”

Section: Related Studiesmentioning

confidence: 99%

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

View full text Add to dashboard Cite

<span>Multiple Regression-Based Prediction (MRBP) is an emerging calculation to or analysis technique cope with the future by compiling the history of data. The MRBP characteristic will include an approximation for the associations between physical observations and predictions. MRBP is a predictive model, which will be an important source of knowledge in terms of an interesting trend to be followed in the future. However, there is impairment in the MRBP dataset, wherein each form of missing and noisy data has caused an error and is unavailable further analysis. To overcome this unavailability, so that the data analytics can be moved on, two treatment approaches are introduced. First, the given dataset is denoised; next, listwise deletion (LD) is proposed to handle the missing data. The performance of the proposed technique will be investigated by dealing with datasets that cannot be executed. Employing the Massive Online Analysis (MOA) software, the proposed model is investigated, and the results are summarized. Performance metrics, such as mean squared error (MSE), correlation coefficient (COEF), mean absolute error (MAE), root mean squared error (RMSE), and the average error percentage, are used to validate the proposed mechanism. The proposed LD projection is confirmed through actual values. The proposed LD outperforms other treatments as it only requires less state space, which reflects low computation cost, and proves its capability to overcome the limitation of analysis.</span>

show abstract

Section: Related Studiesmentioning

confidence: 99%

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

View full text Add to dashboard Cite

show abstract

“…Currently, companies constitute a support for competitiveness in the productive sector [1], [2], companies seek to achieve competitiveness based on the use of various technologies, in order to control the myriad of variables that determine the optimal operation of the industrial process and that guarantee quality production of the manufactured product [3]. Moreover, it is that in the current context in which we find ourselves, which is characterized by globalization and a highly competitive environment for companies, they seek to achieve and show a solid operational performance [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Automatic control of the sand granulation process to improve homogeneity in the manufacture of ceramic tiles

Morales-Romero

Quispe-Andía

Caycho-Salas

et al. 2021

IJEECS

View full text Add to dashboard Cite

The purpose of this article is to quantify to what extent the design of an automatic control system for the clay sand granulation process will improve the percentage of homogeneity in the manufacture of ceramic tiles, through the analysis of the percentage of dust moisture. For which the process under study is initially described, identifying its stages, the control strategy to be used and its main characteristics. After conducting the research, it was determined that from the proposed control strategy, in which the flow of clay sand is controlled through frequency variators and the sequential injection of water spray through electric nozzles, it was achieved improve the homogeneity percentage by 94.78%, achieving that the error between the desired value and the present value of the humidity percentage is 0.015%, the approximation to the desired value or setpoint value being significant. Thus, it was also possible to determine that through an analysis of the dispersion model, that the collected data respond to a constant; evidencing that there is no sudden variation in the percentage of homogeneity during the automated operation of the process, which validates the improvement of homogeneity in the manufacture of ceramic tiles.

show abstract

“…It is an anticipated that this machine is able to collect the LF, segregate the debris and tipping the clean LF into the bin. For future, there are another mechanism that maybe attached to the ILFC machine as a value added [22][23][24][25] such as detection the colour of LF to be collected, monitoring the movement of the ILFC in the field with the intergration of Internet of Things (IoT) and palm oil loader system. This article is focused on the development of ILFC machine whereby the paper is organized as follows; research methods deal with more detailed on the development of ILFC machine and the flow of work in collecting the LF.…”

Section: Introductionmentioning

confidence: 99%

Development of integrated loose fruit collector machine for oil palm plantations

Yusoff¹,

Zamri²,

Kadir³

et al. 2019

Bulletin EEI

View full text Add to dashboard Cite

Oil palm loose fruit have always been part of oil palm harvesting operation. The current manual loose fruit collection by hand picking or raking is contributing to the minimum productivity and large amounts of debris. Effort in future shall be explored for the potential machine or system to collect the loose fruit in the field with the result of clean loose fruit. A new machine for loose fruit collection is being developed and examined which knows as Integrated Oil Palm Loose Fruit Collection Machine for collecting loose fruit from the palm base. Consist of three main integrated stages; the suction stage is used to collect the loose fruit, followed by segregating stage, which used to segregate the loose fruit from debris through double layer rotating drum and unloading stage of loose fruit to the 3-4 metric tons bin by using hydraulic tipping cylinder. Result indicates that at least more than 50% productivity of loose fruit collection were increased. In addition, the clean loose fruit collection resulted with the debris less than 10%. Hence, with this development, better quality loose fruits are collected with minimum debris for further processing in the palm oil mill, which will contribute to better oil extraction rate.

show abstract

e-PADI: an iot-based paddy productivity monitoring and advisory system

Cited by 12 publications

References 10 publications

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Automatic control of the sand granulation process to improve homogeneity in the manufacture of ceramic tiles

Development of integrated loose fruit collector machine for oil palm plantations

Contact Info

Product

Resources

About