Development of Robotic Structure for Image Acquisition and Classification (ERACI) of Sugarcane Plantations
Description
This repository contains source code, data and images, objects of research. This research covered studies on sugarcane, robotics, computer vision, distributed systems, digital image processing, Artificial Intelligence, Machine Learning and Raspberry Pi. The hypothesis considered was whether it is possible to create a robotic system capable of allowing the acquisition, storage, and pattern recognition in images, by means of software that uses computer vision to analyze an image and, basically, detect the presence of sugarcane and weed, as well as the absence of any plant using open source software and hardware, basically intended for school use and with a modular feature. In order to develop a robotic system capable of creating a computer vision system capable of analyzing an image and basically detecting the presence of sugarcane and weed, as well as the absence of any plant, the project developed unified knowledge on these two areas of computer science with the area of robotics and agriculture, which culminated in the development of a robotic structure with free tools, such as software and modular hardware aimed at teaching computer science in schools. Digital agriculture has contributed to improving efficiency in the application of inputs or planting in a predetermined location, resulting in increased productivity. In this reality, the application of Digital Image Processing techniques, as well as the use of systems that use Artificial Intelligence, has increasingly gained the attention of researchers who seek their application in the most diverse media. In order to develop a robotic system capable of creating a computer vision system capable of analyzing an image and basically detecting the presence of sugarcane and weed, as well as the absence of any plant, the project developed unified knowledge on these two areas of computer science with the area of robotics and agriculture, which culminated in the development of a robotic structure with free tools, such as software and modular hardware aimed at teaching computer science in schools. The combination of all this resulted in a software and hardware structure capable of allowing the capture and storage of images in a database; in addition to enabling the classification of images by users enabled through the Android application. By checking the accuracy delivered by the Machine Learning algorithms with cyclic injection and analyzing the response time, it was found that the system was able, with this information, to generate classifiers that are remotely loaded by the RRD and these, in turn, were able to classify images in sugarcane fields in real time.