With the advent of the latest sensing technologies, agricultural tasks can be performed so quickly andadequately and termed Smart agriculture. In this paper, a system based on sensor networks has been designed tomonitor agricultural parameters wirelessly. The proposed system has been deployed in a Wheat field. The aim of this workis to increase the quality and productivity of the Wheat crops and minimize the extensive field visits of the farmers. Thissystem enables precision agriculture by periodically measuring the three most key parameters (temperature, light, andwater level) for achieving a remarkable increase in quality, productivity and growth of the Wheat crops. Thus, this systemhelps the agriculturists, landowners and research experts to monitor these parameters at the base station without goingto the field site. A GUI tool is also designed to display the measured data and stored it in the database accordingly. Whiledesigning this system; IRIS mote, MDA100 data acquisition board, and MIB520 USB interface board are employed. Weuse TinyOS operating system for the development of codes for wireless nodes and the GUI tool is designed in MicrosoftVisual Studio. ZigBee IEEE 802.15.4 protocol and direct topology are used for the communication of nodes with the basestation.

Water is a constrained asset and basic requirement for animals, cultivation and industry that exist on this Earth including mankind. Subsequently, to measure the characteristics like physical, biological and chemical attributes of water becomes indispensable. Floating sensor networks (FSN) with in-situ sensors are now widely used to gather the water quality data. The main goal that deals with FSNs is data reliability, congestion control, optimal node placement and energy. So far, we proposed a generic framework for energy harvesting, reliable data transform congestion detection and deployment strategy for FSN to collect water quality data. Energy harvesting subsystem model will be designed to fulfil the needs for the desired application scenarios in sensor networks and extend the network lifetime. Therefore, this model will help in scheduling the sending/receiving, sleeping and idle time of the node. By incorporating this framework, network lifetime can be prolonged. For reliable data transform and congestion detection; a model will be developed to enhance the data delivery. The objectives of optimal node deployment are full coverage, network connectivity, improve the data fidelity and enhance the network lifetime.