The C₂hg Model based Multi Attribute Crop Yield Estimation and Water Regulation System for Indian Soil using Artificial Neural Network

The environmental change of world affects the growth of crops in many ways where any crop has its own environmental conditions and needs other factors like hydrology, climate, crop pattern and geological information. This also affects the crop yield in many ways, and the hydrological factors are the most affecting reasons for plant growth. To solve the problem of crop yield and to improve the plant growth, a C₂HG-(Climate-Crop-Hydrology-Geology) model has been discussed. The method maintains the pattern of crop yield at different time window in the past, and each pattern has been initialized with a neuron with various factors and functions. The patterns are submitted to the artificial neural network where each pattern has been initialized with neuron and performs various functions to produce the final result. Each neuron estimates possible crop yield and computes the amount of water to be regulated using multi attribute water regulation scheme. The estimated values are passed through neurons of different layers of ANN. The method estimates the plant growth using the patterns of hydrology, climate, crop and geological information. The method produces a more efficient result in helping the plant growth for the development of agricultural industries.