This paper describes an experimental study on the effect of reducing time series collected from IoT electrical agro-sensors through approximation techniques, in time series classification tasks, for plant stress detection. From large sets of real data, stored in time series format, experiments were carried out to analyze: (i) performance of mathematical methods to reduce the dimensionality of time series - PAA, SAX and MCB; and (ii) Whether the application of these techniques influences the performance of time series classification models for plant stress detection, using machine learning algorithms KNN, SVM and ANN. Both in terms of data volume reduction and time series classification, the experiment showed significant improvements in terms of compression rate and accuracy, with the best result found in the use of PAA+SAX techniques for reduction and SVM for classification.