Iot based Agriculture Drought Prediction using Chaotic Genetic Algorithm Integrated Intuitionist Fuzzy Subtractive Clustering - Margaret, M. Rose; Pavithra, Dr. L. (International journal of recent technology and engineering, 2019)
The exponential demand in usage of internet of Things (IoT) devices, there is a vast effective improvement in commination among different things. Especially in the field of agriculture, IoT based applications plays a vital role to make the functionalities more reliable. With the perception of IoT and wireless sensor network, smart intelligent farming system has become a significant research area for researchers. Several researchers have developed automation and monitoring system for various agricultural functionalities. One of the serious issues is agricultural droughts which affect crop production or the ecology of the range. This research work aims to overwhelm this issue positively by enhancing the agriculture drought prediction in India. This proposed technique enriches the quality of the dataset by finding the similar patterns using chaos genetic algorithm based Intuitionistic fuzzy Subtractive Clustering. The uncertainty in drought prediction is greatly handled by representing the dataset in the form of intuitionistic fuzzy domain which gives more importance to the degree of indeterminacy. Intuitionistic fuzzy inference system is enhanced with the knowledge of subtractive clustering. The cluster centroids are selected by the chaotic genetic algorithm,which overcomes the earlier convergence and increase the search space in a parallel manner to handle voluminous agriculture dataset. Feed forward neural network is used for predicting the clustered agriculture dataset to provide intelligent smart solution for drought prediction and to improve the crop growth monitoring task by farmers.