## 1 Introduction
Each one of the extracted feature values is compared with wide and narrow ranges defined in the signature of a specific disease. A different grade is given if a feature value resides within the narrow or the wider range of the corresponding disease feature, otherwise a zero grade is given. The weighted sum of the grades assigned for each feature ranks the likelihood of a specific disease. This classification method can be characterized as fuzzy-like clustering with discrete ranks assigned to each cluster (disease). The novelty in the employed classification method stems from the combination of the specific features used (e.g., the similarity of the extracted histograms is determined by the beginning, the ending and the peak of the lobes) and the comparison of each feature value with multiple ranges. The simplicity of the employed classification method allows the full implementation of the proposed method on a smart phone that does not have to be connected to a remote server as is the case, for example, in [21]. The simplicity is also necessary for the extensibility of the supported set of diseases by the administrator of the application instead of its developer, without having to train the system with numerous images and image patches. As will be demonstrated by the experimental results and the comparison with other popular classification algorithms, the selected classification method achieves very good results despite its simplicity. The disease signature can be defined by an end user that does need to have access to the application source code and does not have to be familiar with its architecture either. This makes the application extensible since it is capable of supporting an unlimited number of plants and diseases. A relatively small training set consisting of representative photographs can be used to define a disease signature. The recognition accuracy may be low if a very small number of training photographs are used due to extremely different environmental conditions or varieties of the same plant that may appear in a new photograph. However, a small training set may be adequate for the recognition of diseases that appear in the same geographical region where the training was carried out. The missing information due to the small training set is compensated by the large number of features used in the disease signatures. Although the training set size scales up with the size of the test set, it is expected that a limited number of training photographs will be sufficient to cover all the cases. The recognition algorithm is fast and does not consume a large number of mobile phone resources. The approach presented in this paper is an extension of the application described by the author in [26; 27; 28] (see Video S1) where a minimal set of features used for disease recognition was defined. Some initial experiments have been performed in these papers using grape, peach, or citrus leaves. The approach presented here differs from our previous work in the following: (a) the disease signatures are extended (the color features of the halo around the spots are taken into consideration), (b) the disease recognition method is described in detail, (c) automatic segmentation methods (for background separation and lesion spot recognition) are introduced and evaluated, (d) the extensibility of the system is assisted by tools that automate the disease signature definition, (e) extensive experimentation with citrus diseases is carried out under different light exposure using photographs of upper or lower leaf surface and orange fruits, (f) color normalization issues are discussed to minimize the effect of different light exposure, (g) the specific features used in the proposed classification scheme are tested with a number of popular classification algorithms (decision trees, random forests, neural networks, etc.) and the results are compared with the ones achieved by the proposed classification scheme.