This research presents an efficient fruit monitoring and orchard management system using RFIP (Radio Frequency IDentification) technology. The new method is expected to replace the existing paper-based manual process. Each tree is attached with a DFID tag, and each fruit, mango in this research is attached with a RFID tag as well. An RFID reader was developed for scanning the RFID tags and sending the treefruit group data to a cloud database. The cloud server runs the process and inventory management. The RFID reader is multi-functional and it is used by the workers in carrying out tasks such as covering fruits with protective bags at the early stage, spraying, and plucking. In the cloud-based management system, the workers are efficiently assigned to these tasks. Each worker has a field device that shows the assigned tasks. When the tasks are accomplished, the status is updated in the cloud database. Using this system, each fruit is monitored from the initial covering state to the final plucking state including timely spraying of agro-chemicals.

The Kalman filter is often used with plants without zeros. However, when the plant has zeros, the Kalman filter tends to neglect the effects of zeros, leading to errors in output estimation. Without the effects of zeros, the Kalman filter estimation would lag behind the true output, or it would lead to true output if the zeros are on the RHP. The parallel Kalman filter bank, instead of a single filter is proposed to remedy this problem. Each Kalman filter in the bank operates on a plant submodel that is obtained by decomposing the plant model. The output estimations of all of the Kalman filters in the bank are added together to synthesize the plant output estimation. The method was simulated in a PID-controlled system where better results have been obtained.

The quadrotor is an under-actuated nonlinear system that poses challenges in modeling and controller design. This work uses the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor to build a decoupled control system. A linear quadratic Gaussian (LQG) regulator is used as the controller, and two extended Kalman filters are used for state estimation in the two dynamic models. The entire design process from modeling to flight simulation is presented. Model linearization while preserving controllability, state-space modeling under gravity disturbance, feedforward compensation, LQR gain adjustment, actuator dynamics, sensor system for observability, adaptation of extended Kalman filter, and its limitations are presented.

This paper compares the two take-off methods; vertical take-off and bird take-off for quad-plane unmanned aerial vehicles. Energy consumption and flying quality of the two methods are assessed. Often, the quad-plane aerial vehicles take off vertically using the quad mode, and when the reference altitude is reached the plane mode kicks in. The rarely used bird take-off mode, uses both quad and plane modes kick in together at the take-off so that the UAV gains the altitude and airspeed symultaneously lower energy. The experiments were carried out with a 1.35kg quad-plane on a two short flights conducted back-to-back under similar environmental conditions. It has been found from the flight data that the bird take-off helps quad-plane to fly 10.8\% more distance for a unit of energy than with the vertical take-off. It was also observed that the VTO method shows sudden altitude drop during the quad to plane control transition.