Predictive models for smart vineyards

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.

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.