Identifying Vineyard Diseases using Image Processing and Machine Learning

In this Master Thesis we aimed to develop a model that will enable the identification of the health condition of vines from images taken by a UAV, with the utilization of image processing and Machine Learning.
To achieve this task, we prepared a training dataset containing images of fungal-infected and healthy vine leaves and we tested several Machine Learning and a Convolutional Neural Network models to determine the optimal solution in terms of health condition classification accuracy. We then applied the selected classifier on a set of UAV images captured from five controlled vineyards, obtaining the model’s predictions on the vines’ health condition.
The developed model aspires to provide a solution for vine growers who would be able to have a better control of their plants status, allowing quicker identification and cure of potential threats from diseases and safeguarding their vineyard’s yield.