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<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

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The mission of automatically recognizing different facial expressions in human-computer environment is significant and challenging. This paper presents a method to identify the facial expressions by processing images taken from Facial Expression Database. The approach for emotion recognition is based on the texture features extracted from the gray-level co-occurrence matrix(GLCM) . The results show that the features are highly efficient to discriminate the expressions and require less computation time. The extracted GLCM features are trained with Support Vector Machine using different kernels to recognize the basic emotions Happy, Disgust, Surprise and Neutral.

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