  Generally, the identification and classification of plant diseases and/or
pests are performed by an expert . One of the problems facing coffee farmers in
Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and
leaf miner Leucoptera coffeella. The progression of the diseases and or pests
occurs spatially and temporarily. So, it is very important to automatically
identify the degree of severity. The main goal of this article consists on the
development of a method and its i implementation as an App that allow the
detection of the foliar damages from images of coffee leaf that are captured
using a smartphone, and identify whether it is rust or leaf miner, and in turn
the calculation of its severity degree. The method consists of identifying a
leaf from the image and separates it from the background with the use of a
segmentation algorithm. In the segmentation process, various types of
backgrounds for the image using the HSV and YCbCr color spaces are tested. In
the segmentation of foliar damages, the Otsu algorithm and the iterative
threshold algorithm, in the YCgCr color space, have been used and compared to
k-means. Next, features of the segmented foliar damages are calculated. For the
classification, artificial neural network trained with extreme learning machine
have been used. The results obtained shows the feasibility and effectiveness of
the approach to identify and classify foliar damages, and the automatic
calculation of the severity. The results obtained are very promising according
to experts.
