Machine Learning Algorithms for Service Robotics Applications in Precision Agriculture

World population is growing faster than expected. Every year Earth resources are consumed faster, as proven by the shift back of the Earth Overshoot Day that claims the end of year resources. One of the simplest solutions to this problem would be investing in technologies and innovations towards a smart extraction of what population needs. In the last few years a lot of companies introduced automation and robots to improve production in terms of time and obviously cost. Agricultural world is not distant from this evolution; in fact many of the works attempted are now helped by a set of machines that simplifies human work. Hitherto the application in precision agriculture has been always under the human control; there are a lot of application that sees the participation of a worker and in parallel a machine used for a lot of tasks. In the future may an entire field will be manage by a group of automated robots that works together. Those machines will requires a lot of specific features likes autonomous navigation, mapping, visual object recognition and many others. This thesis is part of the project and it regards the detection and classification of fruits for future application of auto-harvesting and health control. In a more specific way there will be treated apples as fruit application and will be addressed methods and algorithms as Y.O.L.O. and Mask R-CNN to do the processing of images. With the application of these techniques it is possible the detection of apples in post processing and in real-time with accuracies that range over 20% to 98%. The final result can be used in the future application for the spatial localization of fruits and the detection of possible disease. It should be emphasised that also if the thesis show the results of the object class apple the algorithms can be applied in a wide range of object with the only requirements of a different training images dataset.