Ms Almira Suljovic, a student of the Faculty of Applied Sciences, defended her BSc Thesis in Electrical Engineering and Computer Science. The topic of the thesis work was the use of machine learning to detect diseased plants by processing images of the leaves. The work included creation of a prediction model as well as the integration of the model into a mobile application for the use by farmers. She has done her thesis work under the supervision of prof. Tomo Popovic, PhD, and mr Stevan Cakic, MSc.
ABSTRACT – Early detection and prevention are one of the biggest difficulties in the field of agriculture. Late detection of plant diseases or the use of wrong pesticides often leads to crop damage, reducing food quality. As the leaf is the best indicator of whether a plant is healthy or not, we can construct prediction models using machine learning to identify leaf status in the shortest possible time, thus preventing or reducing losses. This thesis shows how to determine leaf health status using the Detectron2 software library and the faster R-CNN neural network. The model was trained using a dataset with 6407 images. The initial dataset was extended using the RoboFlow tool. Google Colab, an environment for the development of cloud computing and machine learning, was used for testing and implementation. The practical application of the machine learning model was realized using an application developed using the Flutter platform.