Recently, quadcopters with their advance sensors and imaging capabilities have become an imperative part of the precision agriculture. In this work, we have described a framework which performs plantation monitoring and yield estimation using the supervised learning approach, while autonomously navigating through an inter-row path of the plantation. The proposed navigation framework assists the quadcopter to follow a sequence of collision-free GPS way points and has been integrated with ROS (Robot Operating System). The trajectory planning and control module of the navigation framework employ convex programming techniques to generate minimum time trajectory between way-points and produces appropriate control inputs for the quadcopter. A new 'pomegranate dataset' comprising of plantation surveillance video and annotated frames capturing the varied stages of pomegranate growth along with the navigation framework are being delivered as a part of this work.

Automatically describing videos has ever been fascinating. In this work, we attempt to describe videos from a specific domain -broadcast videos of lawn tennis matches. Given a video shot from a tennis match, we intend to generate a textual commentary similar to what a human expert would write on a sports website. Unlike many recent works that focus on generating short captions, we are interested in generating semantically richer descriptions. This demands a detailed low-level analysis of the video content, specially the actions and interactions among subjects. We address this by limiting our domain to the game of lawn tennis. Rich descriptions are generated by leveraging a large corpus of human created descriptions harvested from Internet. We evaluate our method on a newly created tennis video data set. Extensive analysis demonstrate that our approach addresses both semantic correctness as well as readability aspects involved in the task.

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