This paper presents the impact of machine learning in precision agriculture. State-of-the-art image recognition is applied to a dataset composed of high precision aerial pictures of vineyards. The study presents a comparison of an innovative machine learning methodology compared to a baseline used classically on vineyard and agricultural objects. The baseline uses color analysis and can discriminate interesting objects with an accuracy of (89.6 %). The machine learning, an innovative approach for this type of use case, demonstrates that the results can be improved to obtain 94.27 % of accuracy. Machine Learning used to enrich and improve the detection of precise agricultural objects is also discussed in this study and opens new perspectives for the future of high precision agriculture.

This paper presents the performances of machine learning algorithms on aerial images object detection for high precision agriculture. The dataset used focuses on geotagged pictures of vineyards. We demonstrate that advanced machine learning methodologies like Decision Tree Ensemble, outperform state-of-the-art image recognition algorithms generally used within the agriculture field. The innovative approach described here improve object detection and obtain an accuracy of 94.27 % which is an increase of more than 4 % compared to the state-ofthe-art. Finally, methodology and possible developments for high precision agriculture are discussed in this study.

This paper presents a study of the efficiency of machine learning algorithms applied on an image recognition task. The dataset is composed of aerial GeoTIFF images of 5 different vineyards taken with a drone. It presents the application of two different classification algorithms with an efficiency comparison over a small dataset. A Neural Network algorithm for classification through the TensorFlow platform will be explained first, and a Decision Tree Ensemble algorithm for classification through a machine learning platform will be explained second. This work shows that the accuracy of the Decision Tree Ensemble algorithm (94.27 %) outperforms the accuracy of the Deep Learning algorithm (91.22 %). This result is based on the final detection accuracy as well as on the computation time.

In this paper, a model for traffic jam prediction using data about traffic, weather and noise is presented. It is based on data coming from a Smart City in Spain called Santander. The project in this city is called "Smart Santander" and provides a platform for large-scale experiment based on realtime data. This paper demonstrates the possibility of predicting traffic jams and is a basis to integrate in projects to improve the quality of services. In this work, a cross validation method to ratify our training set is proposed. Data intelligence analysis techniques are used for the prediction with an implementation of Neural Network and Decision Tree algorithms. These algorithms are using different parameters coming from Smart Santander and other external sources. Furthermore, a cross validation process is also integrated to improve the final result. The traffic jam prediction for the next 15 minutes reached an accuracy of 99.95%.

The building energy consumption represent 60% of total primary energy consumption in the world. In order to control the demand response schemes for residential users, it is crucial to be able to predict the different components of the total power consumption of a household. This work provide a non intrusive identification model of devices with a sample frequency of one hertz. The identification results are the inputs of a model to predict the flexible energy. This corresponds at the different devices could be shift in a predetermined time. In a residential building, the heating and the hot water represent this flexible energy. The Support Vector Machine (SVM) enable an identification around 95% of heating, hot water, household electrical and a ensemble of decision tree provide the prediction for the next 15 minutes.