Agriculture is the primary source of food production for humankind. The drastic increase in the global population is paving the way to directly increase the food productivity creating enormous pressure on the agriculture sector. The demand for food production is increasing rapidly every day across the globe. According to the UN world population index, the world population is expected to reach 9.7 billion by 2050. Doing traditional conventional farming will not be sufficient for the production and catering to the needs of 9.8 billion people. We are now shifting from conventional traditional farming to new advanced precision farming methods to meet the demand. The introduction of the Internet of things (IoT) into the agriculture sector has changed the dynamics of the sector. The IoT based devices are mainly proving to be efficient in giving a high performance with low energy usage. IoT based devices automatically monitor and maintains agricultural farms reducing the minimal use of human involvement. This article mainly highlights the recent IoT based products and their usage in the agriculture sector. The public and private projects across the globe which provide feasible growth in the agriculture sector are discussed. Further, this article discusses the use of unmanned aerial vehicles (UAV) and their applications in the agriculture sector. After a thorough review of the future of IoT in the agriculture sector, its Applications, challenges, research potential and limitations are briefly discussed.

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Greater reliance on smart and portable electronic devices demands engineers to provide solutions with better performance and minimized demerits. Face Recognition involves the method of associating and confirming the faces. It is fit for distinguishing, following, recognizing, or checking human appearances from a picture or video caught utilizing an advanced camera. Feature extraction is the most significant stage for the achievement of the face recognition framework. The different ways of implementing this project depends on the programming language or algorithms used such as MATLAB, OpenCV, visual basics C#, Viola-Jones algorithm and many more while the core functioning remains the same. In this work, we have implemented face recognition in 3 phases, Phase1 consists of detecting faces and collecting images IDs, Phase 2 involves training the Recognizer and Separating interesting elements and the final phase includes grouping them and putting away in XML records.

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