dc.contributor.advisor | Niesler, T. R. | en_ZA |
dc.contributor.author | Luttich, F. R. | en_ZA |
dc.contributor.other | Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. | en_ZA |
dc.date.accessioned | 2019-02-22T06:49:50Z | |
dc.date.accessioned | 2019-04-17T08:20:30Z | |
dc.date.available | 2019-02-22T06:49:50Z | |
dc.date.available | 2019-04-17T08:20:30Z | |
dc.date.issued | 2019-04 | |
dc.identifier.uri | http://hdl.handle.net/10019.1/105950 | |
dc.description | Thesis (MScEng)--Stellenbosch University, 2019. | en_ZA |
dc.description.abstract | ENGLISH ABSTRACT: We investigate the application of machine learning algorithms to the predictive
analysis of environmental datasets compiled from two distinct vineyards. These
datasets include the soil temperature at various depths and locations, the soil
moisture content of the same locations and the bud-burst dates. Measurements
were taken regularly over the space of four months for one vineyard and over
twelve months for the other.
The prediction of the soil temperature from either ambient measurements or
from satellite data, as well as the prediction of soil moisture content and the
bud-burst dates were the primary objectives of our analysis. Linear regression,
feedforward neural networks and recurrent neural networks were considered as
algorithms. For the neural networks, several training strategies were considered.
It was found that neural networks outperform linear regression when predicting
soil temperatures from ambient temperature and humidity, and also when predicting
soil moisture content from ambient temperature, humidity and rainfall data. Although
recurrent neural networks (LSTMs) were able to achieve even better results when
the data was carefully prepared, these networks were sensitive to discontinuities
present in the data due to faulty sensor measurements. Feedforward neural networks,
on the other hand, were more robust to these errors. Since sensors placed in a
vineyard are exposed and must remain unattended, this is an important aspect to
consider. It was also found that soil temperatures could be predicted with a modest
loss in accuracy from freely-available satellite land temperature measurements.
Although cloud cover leads to sporadic non-availability of the measurements, they
represent a very attractive alternative to locally installed weather sensors since
they would no longer need to be installed or maintained.
For soil moisture content and bud-burst dates neural networks provided better
predictions than a na ve guess. While this indicates potential for such models,
these results must be re-examined using a larger dataset.
Although this thesis presents only preliminary results due to the lack and small
size of suitable datasets, our results nevertheless clearly indicate the potential of
machine learning techniques to assist viticulture. | en_ZA |
dc.description.abstract | AFRIKAANSE OPSOMMING: In hierdie tesis ondersoek ons die toepassing van masjienleer algoritmes op die
voorspellende ontledings van omgewings data stelle saamgestel uit lesings van
twee verskillende blokke wingerde. Hierdie data stelle sluit lesings van die grond
temperatuur op verskillende dieptes en areas, ondergrondse water inhoud en die
\bud-burst" of bloeisel datums in. Data was versamel oor 'n tydperk van vier
maande vir die een blok en oor twaalf maande vir die ander blok wingerd.
Die voorspelling van grond temperatuur, vanaf of die omgewings temperatuur,
of vanaf satelliet data, asook van die grond vog inhoud en die bloeisel datums
was die prim^ere doelwitte van ons ontledings. Line^ere regressie, vorentoe-voerende
neurale netwerke (VVNNe) en wederkerende neurale netwerke (WNNe) was oorweeg
as algoritmes. Vir die neurale netwerke was verskeie opleidings strategi e oorweeg.
Dit was gevind dat neurale netwerke, line^ere regressie oortref met voorspelling
van grond temperature vanaf omgewings temperature en humiditeit, asook met die
voorspelling van grond vog inhoud vanaf omgewings temperatuur, humiditeit en
re enval data. Alhoewel wederkerige neurale netwerke selfs beter resultate gelewer
het wanneer die data stelle noukeurig voorberei was, was hierdie netwerke sensitief
vir diskontinu teite in die data as gevolg van foutiewe sensor lesings. Die VVNNe,
aan die ander kant, was meer robuus. Aangesien sensors in wingerde blootgestel
word aan die elemente, en hulle sonder toesig moet funksioneer vir uitgerekte
periodes, is hierdie 'n belangrike aspek om te oorweeg in enige formulerings.
Dit was ook gevind dat voorspellings rakende grond temperature, voorspel kon
word met 'n minimale verlies aan akkuraatheid vanaf vrylik beskikbare satelliet
land-oppervlak temperature. Alhoewel wolkbedekking lei tot sporadiese onderbreking
van die lesings, bly dit 'n aantreklike alternatief tot lokale weer sensors, aangesien
hulle nie op grondvlak ge nstalleer of onderhou hoef te word nie.
Grond vog lesings en bloeisel datums kon meer akkuraat voorspel word as
'n na ewe raaiskoot. Alhoewel hierdie bevindinge aandui dat hierdie bevindinge
potensiaal inhou, moet hierdie resultate her-evalueer word met groter data stelle
vir beter betroubaarheid.
Hierdie tesis verteenwoordig slegs voorlopige resultate, as gevolg van die gebrek
aan groot genoeg en geskikte data stelle, maar steeds dui ons resultate duidelik
die potensiaal van masjienleer tegnieke om wingerd-en-wynkunde beplannings by
te staan in die ontwikkeling van meer betroubare resultate. | af_ZA |
dc.format.extent | 122 pages : illustrations | en_ZA |
dc.language.iso | en_ZA | en_ZA |
dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
dc.subject | UCTD | en_ZA |
dc.subject | Machine learning | en_ZA |
dc.subject | Model based predictive control | en_ZA |
dc.subject | Data sets | en_ZA |
dc.title | Predictive models for smart vineyards | en_ZA |
dc.type | Thesis | en_ZA |
dc.rights.holder | Stellenbosch University | en_ZA |