IOT Based Strategic Solution for Tea Leaves Quality Optimization Using Machine Learning Data Model Predictions for Local Tea Industry

Simple item page
dc.contributor.authorHewawasam, H. P. M. M.
dc.contributor.authorKalubowila, U. K. K.
dc.contributor.authorKiyas, K. M. M.
dc.contributor.authorWickramarathne, S. D. H. S.
dc.contributor.authorEllepola, C. H. D.
dc.date.accessioned2021-02-01T06:40:24Z
dc.date.available2021-02-01T06:40:24Z
dc.date.issued2020
dc.description.abstractEnvironmental factors play a major role in tea growing and plucking stages and these factors must have to be within the favourable range to get quality tea production. At present tea, pluckers cannot identify the exact duration for tea plucking and they do not have sources to identify and pick tea leaves from tea buckets without overflowing which can cause physical damages to tea leaves. This research addresses the above issues by creating data forecasting models that provide significant guidance to make decisions in many areas especially in tea cultivation, plucking, and transportation. Three devices were developed to capture real-time weather data namely soil PH, surface temperature, and Humidity. Above sensors data were transmitted over GPRS using a GSM module. Evaluated results of datasets with actual data values and analysed with different prediction algorithms such as Voted perceptron, Decision table algorithm, Multilayer perceptron, and Simple linear regression. After observing all the aspects, several variables, and prediction accuracy for data samples, the most relevant algorithm to build the prediction models were decided. The models were executed with a different combination of factors and analysed the output prediction result to sort the most accurate factor combination for the dataset. Models were built to predict the most suitable periods having optimum environmental conditions to pluck tea leaves, production forecasts by considering environmental and soil conditions, and transport scheduling for plucked tea leaves before quantity overflows. Above mention, the model was helped to schedule the plucking process while enhancing the quality of tea leaves. Further, this study introduced the smart tea plucking basket to control the realtime weather conditions and reduce human malpractices while maintaining optimum quality. The research recommends assessing this model with different algorithms to fine-tune the performance and to build a general model that can be applied when enhancing other quality factors. Keywords: Internet of Things (IOT), Voted perceptron, Decision table algorithm, Multilayer perceptron, Simple linear regressionen_US
dc.identifier.isbn9789550481293
dc.identifier.urihttp://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/5717/proceeding_oct_08-194.pdf?sequence=1&isAllowed=y
dc.language.isoenen_US
dc.publisherUva Wellassa University of Sri Lankaen_US
dc.relation.ispartofseries;International Research Conference
dc.subjectAgricultureen_US
dc.subjectTea Industrialen_US
dc.subjectComputer Scienceen_US
dc.subjectInformation Scienceen_US
dc.subjectComputing and Information Managementen_US
dc.titleIOT Based Strategic Solution for Tea Leaves Quality Optimization Using Machine Learning Data Model Predictions for Local Tea Industryen_US
dc.title.alternativeInternational Research Conference 2020en_US
dc.typeOtheren_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
proceeding_oct_08-194.pdf
Size:
30.76 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
International Research Conference of UWU-2020