Disease recognition has been huge research area nowadays because inspection of quality of fruits at an early stage prevents spreading of disease to the other areas of fruit as well as helps to reduce great economic losses in agricultural sectors and industries. Different types of diseases exist in different fruits. The focus of the present research work is on quality evaluation of apple fruit. The basic process for defect detection in fruits is basically divided into two major steps; feature extraction and classification .Feature extraction involves extracting features like color, texture and shape from fruit image. The output of this are feature vectors which are given as an input to the classifier. Finally, the classifier categorizes them into appropriate classes. The accuracy of this process depends on many factors like number of input images, method chosen for pre processing, features extracted, classifier chosen, etc.

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Intelligent video surveillance system are extensively used in each and every sector of business. Ranging from small shops to safety systems, surveillance has become an integral part. In these fielded systems, a variety of factors can cause camera obstructions and persistent view change. The view change may adversely affect their performance. Examples include intentional blockage, noise, frame freeze, etc. which might warrant alarms. Considering the fact that the intelligent surveillance system is with very less human intervention, it is important to efficiently classify the tampered video. Analysis of the tampered videos helps in further scene investigation. The goal of the project is to use Support Vector Machines (SVM) a machine learning technique which classifies the real-time videos based on features extracted. The features selected are histogram gradients, HSV (Hue Saturation Value) and RGB (Red Blue Green) for the color based classification and edges (edge weight and direction) for the texture based classification. Further improvements are done using a deep learning technique such as CNN. Convolution neural networks make use of large amount of training data and use tensorflow framework for classification. The system accepts video inputs in mp3 or avi format. The output is the classification of tampered videos and alarm generation. Comparison between the two methodologies is done. Support vector machines gives an accuracy of 75% and convolutional neural networks give accuracy of 93%. The system is very useful to monitor all the surveillance activities.

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