## 1 Introduction
Reviews of image processing techniques in visible light including classification, real-time monitoring and neural networks can be found in [13; 14]. Image segmentation in the International Commission on Illumination (CIE) L*a*b color space, followed by a Gabor filter to form an input to the neural network that achieves a disease recognition with 91% accuracy, was presented by [PERSON] and [PERSON] in [15]. [PERSON] and [PERSON] [16] applied image processing for disease identification based on shape, texture fractal dimension, lacunarity, dispersion, gray levels, gray histogram discrimination and Fourier descriptors. [PERSON] et al. [17] evaluated two deep convolutional neural networks (AlexNet, GoogLeNet) using thousands of images as a training set with an accuracy higher than 98%. Customized disease detection solutions for specific plants have also been presented (e.g., by [PERSON] et al. for corn diseases [18], [PERSON] et al. [19] for citrus pathogens, etc.). [PERSON] et al. reviewed in [20] some image processing techniques and molecular tests. Molecular tests like the polymerase chain reaction (PCR) technique can be applied as described by [PERSON] and [PERSON] in [19]. The sensitivity of the PCR depends on the amount of the microorganism that is tested. There are not many references in the literature for smart phone applications developed for plant disease diagnosis. A mobile client-server architecture for leaf disease detection and diagnosis using a novel combination of a Gabor wavelet transform and gray level co-occurrence matrix, is proposed in [21]. A diseased patch is processed to extract a multi-resolution and multi-direction feature vector. The mobile client captures and pre-processes the leaf image, segments diseased patches in it and transmits the results to the pathology server which performs feature extraction and classification using k-nearest neighbor with 93% accuracy. In [22], it is stated that the use of image processing can leverage early detection of diseases but they perform poorly under real field conditions using mobile devices. An image processing algorithm based on candidate hot-spot detection, in combination with statistical inference methods, is proposed for disease identification in wild conditions. Seven mobile devices were used to capture and analyze more than 3500 images displaying wheat diseases (_Septoria_, rust and tan spot). The measured \"Area under the Receiver Operating Characteristic-ROC-Curve\" metrics were higher than 0.80 for all the analyzed diseases. A heuristic method based either on graphical representation or a step-by-step descriptive method, was used for plant disease recognition by [PERSON] et al. in [23]. A set of questions are answered by the user in the step-by-step procedure while in the graphical representation, the user compares photographs of the closest disease cases. This approach requires internet connection. The application Purdue Plant Doctor [24] is offered in different versions for each plant type for iOS or Android platforms. It is also based on questionnaire and decision trees in order to diagnose a disease. A similar application to the one presented in this paper is Plant Doctor offered by Plantix [25]. It is an expert system trained to recognize a large number of diseases. It can communicate with a remote database for higher accuracy. The plant disease recognition method described in this paper is targeted for mobile phone applications. The term \"disease\" in this paper refers to any situation that can damage a plant including pathogens, biotic agents, nutritional deficiency, pollution, harmful weather conditions, etc. The proposed method is based on the extraction of various features from a photograph of a plant part. These features include the number of spots, their relative area, the spot, leaf and halo color, weather conditions, etc. Absolute lesion dimensions can be easily estimated and taken into account, although only relative dimensions are used here. The plant part can be a leaf, fruit, branch, root, etc.