On field disease detection in olive tree with vision systems

In the present work the capability of convolutional neural networks to extract samples of leaves in images of tree’s canopy and detect the presence of different diseases and pests that manifest in deformation, discoloration or direct presence in the leaves, is studied. The sample obtained along with its location and sampling date, allows a mapping of the diseases in the field. This mapping capability will allow better decisions to be made when fighting these canopy diseases. An example of those are fungus and Aceria oleae in olive leaves. The study begins with the analysis of a data set generated in the laboratory and divided into healthy and faulty parts. The images were captured with a RGB and a multi-spectral with the blue, green, red, near infrared and red border spectra. They were taken in an image laboratory with a white background and led lighting. The objective was to carry out tests to determine the impact of each spectral channel and the possibility of using different types of cameras for the detection of diseases, as well as important factors to consider for its application in the field. Then, Mask rcnn R 50 FPN 3 was used to obtain segmented leaves and Fast-r cnn inception v2 to detect leaves. Then the detected or segmented leaves were classified with the Inception V3 network to determine which were healthy and which were diseased. With, the combination of these tools, it is possible to determine the disease level of an olive tree in the field.