This paper presents some more advanced topics in image processing and computer vision, such as Principal Components Analysis, Matching Techniques, Machine Learning Techniques, Tracking and Optical Flow and Parallel Computer Vision using CUDA. These concepts will be presented using the open CV library, which is a free computer vision library for C/C++ programmers available for Windows, Linux Mac OS and Android platforms. These topics will be covered considering not only theoretical aspects but practical examples will be presented in order to understand how and when to use each of them.

Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop’s life cycle monitoring. This work presents, which we believe to be the first time, a system capable of generating 3D models of non-rigid corn plants, which can be used as a tool in the phenotyping process. The system is composed by two modules: an terrestrial acquisition module and a processing module. The terrestrial acquisition module is composed by a robot, equipped with an RGB-D camera and three sets of temperature, humidity, and luminosity sensors, that collects data in the field. The processing module conducts the non-rigid 3D plants reconstruction and merges the sensor data into these models. The work presented here also shows a novel technique for background removal in depth images, as well as efficient techniques for processing these images and the sensor data. Experiments have shown that from the models generated and the data collected, plant structural measurements can be performed accurately and the plant’s environment can be mapped, allowing the plant’s health to be evaluated and providing greater crop efficiency.