The exponential growth of population in developing countries like India should focus on innovative technologies in the Agricultural process to meet the future crisis. One of the vital tasks is the crop yield prediction at its early stage; because it forms one of the most challenging tasks in precision agriculture as it demands a deep understanding of the growth pattern with the highly nonlinear parameters. Environmental parameters like rainfall, temperature, humidity, and management practices like fertilizers, pesticides, irrigation are very dynamic in approach and vary from field to field. In the proposed work, the data were collected from paddy fields of 28 districts in wide spectrum of Tamilnadu over a period of 18 years. The Statistical model Multi Linear Regression was used as a benchmark for crop yield prediction, which yielded an accuracy of 82% owing to its wide ranging input data. Therefore, machine learning models are developed to obtain improved accuracy, namely Back Propagation Neural Network (BPNN), Support Vector Machine, and General Regression Neural Networks with the given data set. Results show that GRNN has greater accuracy of 97% (R 2 = 0.97) with a normalized mean square error (NMSE) of 0.03. Hence GRNN can be used for crop yield prediction in diversified geographical fields.
Debureaucratization A complex set of reforms in the public sector, bringing changes especially to the ownership and management of public enterprises. Power The source of electrical energy used for socioeconomic development.
The main objective and an innovative design of this work is to improve the energy efficiency by controlling the variables flow and level in a hydroelectric power plant using Programmable Logic Control (PLC)-Human Machine Interface (HMI) and fuzzy logic approach. This project will focus on design and development of flow and level controller for small scale hydro generating units by implementing gate control based on PLC-HMI and Fuzzy Logic Control (FLC). So far there is no other better performing control scheme, with uncomplicated approach, in order to match and satisfy the dynamic changes in load demand. In this project, FLC will be applied to flow and level control for small scale hydro generating units is proposed. A lab scale experimental setup is made-up as prototype model for flow and level control and simulation outputs were achieved, using PLC-HMI based fuzzy controller scheme. The hardware set up is designed with 5 stages in the tank 1 and 2 stages in the tank 2. Based on the outputs of the level sensors from tanks 1 and 2, the ladder logic will perform. B&R Industrial Automation PLC inbuilt with 24 digital inputs and provides 16 potential free outputs is used to perform control action. Finally, the performance of the proposed scheme is evaluated by simulation results by comparing with conventional controllers output using the data collected from the hydroelectric power plant. The merits of the proposed Fuzzy scheme over the conventional method are spotlighted.
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