The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. Proposed MethodThe classification process was divided into two phases:  Phase 1: Finding the best feature vector for each class;  Phase 2: Create the final classification system from the best results obtained in the previous phase. Phase 1: Finding the Best Feature VectorThis phase is aimed at finding the best feature vector to represent each of the classes to be considered during classification. To achieve this goal the following steps were accomplished: Decomposition of images into multiple channels (R, G, B, H, S, V, I3a, I3b, and GL);  Application of the discrete wavelet transform (DWT) up to the third level;  Computation of the energy for each sub-band and compose the feature vector; Creation of the SVM classification environment;  Listing of the images used for training and testing;  Evaluation of the best feature vectors. Decomposition of the ImageThe decomposition of the images is the first process the system executes. In this stage an image is decomposed into nine channels, namely: R, G, B, H, S, V, I3a, I3b and GL. Application of the Discrete Wavelet TransformDiscrete Wavelet Transform (TWD) decomposition is applied up to the third level. When an image is decomposed as such it will have ten sub-bands, as illustrated in Figure 3. Note that each sub-band is identified by a number between 1 and 10. Region A1 and sub-bands 8, 9 and 10, are generated by the first level of decomposition of the DWT. Region A2 and sub-bands 5, 6 and 7 refer to the second level of decomposition, and the third level is formed by the sub-bands 1, 2, 3 and 4. Computation of the Energy for Each Sub-bandAfter applying the DWT to the three levels, the energy for each wavelet sub-band is computed. Each value obtained is inserted into a feature vector as the one illustrated in Figure 4. The vector in this figure consists of ten elements, each of them is identified by a number corresponding to the number of the sub-band in Figure 3. The energy value computed for each sub-band is stored in the corresponding vector element.Vector Features 1 2 3 4 5 6 7 8 9 10 Figure 4: Example of the structure of a feature vector. Creation of an SVM Classification EnvironmentThe network architecture used is shown in Figure 5. Note that 10 input elements are used. To each input element is assigned the value of the element of the corresponding characteristic vector. In the hidden layer there are a number of neurons (N) equal to the number of training examples, making the net converge...

In the present work we propose a novel label fusion strategy specifically oriented to MRI Brain tumor segmentation studies. The salient aspect of the overall strategy is the use of the fuzzy set framework to manage uncertainty in the visual interpretation process. In particular Fuzzy Connectedness principles are used to merge individual labels and generate segmentation representative of a common agreement. Labels are provided by the experts who are asked to manually trace few representative points within the objects of interest. Starting from these multiple seeds the Fuzzy Connectedness algorithm computes the segmentation. The proposed strategy is naturally oriented to integrate uncertain information and then it is expected to manage dissimilarity among input labels. Interaction is drastically limited with respect to a complete manual tracing and the formal fuzzy framework supports in the overall process of estimation without arbitrary solutions. A set of experiments have been conceived and in the context of MRI Brain segmentation studies.\ud Results obtained prove the reliability of the proposed label fusion strategy that can be considered alternative to conventional Voting Rules