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This paper presents a solution for remote monitoring and sensing of different agricultural parameters that effect the plant growth and productivity. Hardware descriptive language has been used for the implementation of proposed topology on Field Programmable Gate Arrays. The hardware used for this purpose is an Altera board. The simulated results take into consideration the environmental factors such as the humidity, soil moisture content and the temperature. The proposed system continuously monitors the environmental changes for any updates. The system also controls a water motor that is turned on as the system senses the reduction in moisture content. The system implementation on hard wave level show promising results and have been discussed in detailed.

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This paper investigates the effect of data reduction methods in the performance of Wireless Sensor Network (WSN) using a variety of real-time datasets. The simulation tests are carried out in MATLAB for several methods of reducing the quantity of sent data. These approaches are Data Reduction based -Neural Network Fitting (NNF), Neural Network Time Series (NNTS), Linear Regression with Multiple Variables (LRMV), Data Reduction based -"An Efficient Data Collection and Dissemination (EDCD2)" and Data Reduction based -Fast Independent Component Analysis (FICA). The selected algorithms NNF, NNST, EDCD2, LRMV, and FICA are evaluated using real-time datasets. The performance indicators included are energy consumption, data accuracy, and data reduction percentage. The research results show that the selected algorithm helps to reduce the amount of data transferred and consumed energy, but each algorithm performs differently depending on the dataset used.

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