In this research, plant pathogens are considered as big data because of the numerical counts for high intensity pixels in the images. The research presents an automated approach for early detection of plant diseases using image processing techniques. By analyzing the color features of leaf areas, the k-means algorithm for color segmentation and the Gray-Level Co-Occurrence Matrix (GLCM) are used for disease classification. A novelty of this research is that it illustrates four categories of plants to analyze and compare: (1.) Grain, represented by Rice Plant Leaf Data; (2.) Fruit, represented by banana plant leaf data, (3.) Flower, represented by sunflower plant leaf data; and (4.) Vegetable, represented by potato plant leaf data. Six stages of image processing are applied to real data for diseases of leaf smut for rice, black sigatoka for banana, leaf scars for sunflower, and late blight for potato. Finally, a comparison of the image processing for each of the four plant types, conclusions, and future research directions are presented.
In the field of computer graphics, accurate representation of material properties is crucial for rendering realistic imagery. This paper focuses on the bidirectional reflectance distribution function (BRDF) and its role in determining how materials interact with light. The authors review the state of the art in reflectance measurement systems, with a focus on BRDF and bidirectional texture function (BTF) measurement. They discuss practical limitations in measuring multi-dimensional functions and provide examples of how researchers have addressed these challenges. Additionally, they analyse various approaches to converting measured data into practical analytical functions for use in commercial rendering software, including data-driven methods such as neural networks and hybridized approaches.
In this paper, Feature Selection Technique (FST) namely Particle Swarm Optimization (PSO) has been used. The filter based PSO is a search method with Correlation-based Feature Selection (CBFS) as a fitness function. The FST has two key goals of improving classification efficiency and reducing feature counts. Artificial Neural Network (ANN) Based Multilayer Perceptron Network (MLP) and Deep Learning (DL) have been considered the classification methods on 2 benchmark Autistic Spectrum Disorder (ASD) dataset. The experimental result was compared to the non-reduced features and reduced feature of ASD datasets. The reduced feature give up enhanced results in both classifiers MLP and DL. In addition, an experimental study on the exhibitions of these methodologies has been conducted. Finally, a new trend of PSO-MLP and PSO-DL based classification model is proposed.
In brain image classification, feature set reduction is essential to build an optimised feature subset that will lead to precise measurement. In this paper, an improved technique for feature selection by Moth Flame Optimization with Opposition Based Learning (OBL) and Simulated Annealing (OB-MFOSA) is proposed. The OBL strategy is used to create the optimum initial solution, while Simulated Annealing improves the search space. The proposed OB-MFOSA shows improved performance than other well-known existing algorithms by eliminating getting stuck in the local optima. By using this hybrid moth flame optimization, the feature set is reduced to 40%. Also, image denoising is performed by Dual Tree Complex Wavelet Transform (DTCWT) with an improved Log Gabor filtering technique. The filter bank of Log Gabor filter bank is tuned by Genetic Algorithm. The selected features from hybrid MFO algorithm are classified using SVM classifier. Experiments reveal that this hybrid algorithm shows accurate classification outputs than the previous methods.
