Every year farmers experience huge losses due to pest infestation in crops & this inturn impacts his livelihood. In this paper we discuss a novel approach to solve this problem by constantly monitoring crops using video processing, cloud computing and robotics. The paper concentrates in methodologies to detect pests in one of the most popular fruits in the world -the tomato. An insight into how the idea of the Internet of Things can also be conceptualized in this project has been elaborated.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.

In this paper, we propose a robust and an accurate face recognition model which uses the feature extraction capabilities of fractional discrete cosine transform (FDCT). We apply FDCT on the face image database, and use transform coefficients as features. The proposed model is tested on publicly available standard AT&T face database to demonstrate the recognition accuracy. Extensive experimental comparison is provided with the DCT based face recognition algorithm to establish the superiority of the proposed model in terms of recognition accuracy.

Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.