## 2 Materials and Methods  
The disease recognition method described in this paper is based on symptoms appearing on any part of the plant. This part can be the upper or lower surface of a leaf, fruit, branch, or root, etc. The proposed method attempts to locate the diseased region of this plant part which may be lesion spots or simply regions with a different color, indicating infection by a disease. It is assumed here that the normal plant part has a distinct color and the same holds for the spots. Each spot may be surrounded by a halo as is the case with several diseases. For example, the _Diaporthe citri_ saprophyte melanose in citrus trees or the oomycete parasites of downy mildew in grape leaves are identified by several small black spots or larger brown spots, respectively, without a halo (in most of cases). _Alternaria citri_ (_A. citri_) in citrus trees can be identified by a small number of large dark leaf spots surrounded by a thick yellow halo that is brighter than the green color of the normal leaf. Similarly, the colonization of the citrus leaves by the fungus anthracose can be recognized by several dark spots surrounded by a brighter than the normal thin halo (or vice versa). In cases like nutrient deficiency or Esca, several brighter yellow colored regions appear on the whole leaf that can also be considered as spots. The user has to identify if the spots (i.e., the diseased plant parts are brighter or darker than the normal plant part). In the present version of the application it is assumed that, a single leaf or fruit has been captured on a much brighter background to make easier its distinction. Although this may seem restrictive, the brighter background can be created simply by placing the examined part of the plant on a sheet of paper or a marble bench. The background could also be much darker than the analyzed plant part. In both cases, if a pixel of the analyzed plant part has a similar color to the background it may be misinterpreted as background, but this can also be easily handled by taking into consideration the perimeter of the plant part. In many cases the plant part (e.g., the fruit) may have a much darker or brighter color than the leaves behind it. For example, this is the case when focus is given on a citrus fruit having dark green leaves behind it. This can be handled by the supported background separation algorithm without any modification. Another way to avoid the use of a complicated background separation algorithm is to focus on the lesion and take a photograph so close that only the plant part appears with no background at all. Finally, the plant disease diagnosis method presented in this paper can also be tested on photographs that display multiple leaves or fruits. For example, photographs taken close to a citrus tree displaying multiple leaves or fruits with no background can be used. Of course, the details of the lesions will be hidden in this case and different disease signatures based on representative images of this type will have to be defined. In future versions of the developed application more sophisticated background separation algorithms may be incorporated, as long as they are not too complicated, in order to be supported by the resources of a mobile phone. The plant disease diagnosis application presented in this paper was exhaustively tested on citrus diseases and their symptoms on the leaves and the fruits. Three groups of photographs were used. The first group (A) consists of leaves captured under direct sunlight. The second group (B) includes the same leaves photographed under a canopy. The third group (C) consists of orange fruit photographs captured under various environmental conditions (sunlight, cloudy day, shade). In the photographs that were taken under direct sunlight, the colors of the various areas are more distinctive but the shade of the leaf can be confused with a spot. If the photographs are taken under a canopy no intense shadow appears, but the contrast between the colors is reduced.