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.

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The challenges in generating minimum time trajectory and control for generic quadrocopter flying through sophisticated and unknown environment are explored in this paper. The proposed method uses convex programming technique to optimize polynomial splines, which are numerically stable for high-order including large number of segments and easily formulated for efficient computation. Moreover, exploiting the differential flatness of system, these polynomial trajectories encode the dynamics and constraints of the vehicle and decouple them from trajectory planning. The framework is fast enough to be performed in real time and results in solution which is close to time optimal. As control inputs are computed from the generated trajectory in each update, they are applicable to achieve closed-loop control similar to model predictive controller.

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