The use of autonomous UAVs to improve pesticide application in crop fields
André C. P. L. F. de Carvalho
The population growth, increase of need for healthy food and concerns regarding wildlife protection put a strong demand in the improvement of agriculture productivity, reduction of the presence of pesticides in fruits and vegetables and of wildlife contamination. Agriculture production needs the application of pesticides to keep the necessary productivity levels. The use of autonomous aircrafts in the application of pesticides can increase the application precision efficiency, reducing the harm to human beings and nature. Weather conditions, like wind intensity and direction during the spraying, make the aircraft control difficult. This talk will present a the use of autonomous UAVs able to self-adjust their routes when spraying pesticides in crop fields.
André C. P. L. F. Carvalho received his B.Sc. and M.Sc. degrees in Computer Science from the Universidade Federal de Pernambuco, Brazil. He received his Ph.D. degree in Electronic Engineering from the University of Kent, UK. Prof. André de Carvalho is Full Professor at the Department of Computer Science, Universidade de São Paulo, Brazil. He has been in the Program Committee of several conferences in Data Mining and Machine Learning and is a member of Journal Editorial Boards in these areas. His main interests are Data Science, Machine Learning and Data Mining, particular Data Stream Mining and Metalearning.
Big Data Stream Mining
Real time analytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when problems appear or to detect new trends helping to improve their performance. We'll present in this talk Apache SAMOA, a new open source data stream mining that includes algorithms for the most common machine learning tasks such as classification and clustering. It provides a pluggable architecture that allows it to run on Apache Flink, but also with other several distributed stream processing engines such as Storm and Samza.
Albert Bifet is Associate Professor at Telecom ParisTech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He is serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2015, 2014, 2013, 2012), and ACM SAC Data Streams Track (2016, 2015, 2014, 2013, 2012).