Browsing by Author "Luttich, F. R."
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- ItemPredictive models for smart vineyards(Stellenbosch : Stellenbosch University, 2019-04) Luttich, F. R.; Niesler, T. R.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.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.