A NOVEL PERFORMANCE ENHANCED QUANTUM ENTHUSED ARTIFICIAL BEE COLONY ALGORITHM WITH ANN FOR AGRICULTURAL BIG DATA CLASSIFICATION
Agriculture is the backbone of any country needs to be improved a lot to increase its economic rate. The emerging trend in this field is the predicting or forecasting the yield and types of crop to be grown in a particular season. The problems faced in predicting this area are like identifying the availability of water (drought management), climatic changes, lack of awareness of fertilizers to be used for the particular diseases, changes in weather events, seed quality, type of irrigation to be practiced, explorations of pesticides and fertilizers, and minimised due to unexpected natural disaster etc. this research mainly aims to design a model which can predict the production in earlier. The newly design model can classify correctly the type of crop that can be grown in a particular season in a particular area. This prediction results may help the farmers to sow that crop in that season and thereby they increase their yield rate. Big data analytics is the feasible platform to test and measure the crop. In this paper, a novel hybrid Artificial Bee Colony (ABC) with Quantum computing theory (QC) and ANN is proposed for solving continuous optimization problems. The searching capacity and randomness in selecting the population increases when using this ABC algorithm. Using quantum theory with ABC helps to come out of premature convergence parameter and also gives us to find the optimal value. To show the performance of our proposed hybrid QEABC with ANN, a number of experiments are carried out on agricultural dataset and the related results are compared with two other PSO with SVM and ABC with SVM. The experimental results prove that our proposed hybrid QEABC with ANN is reasonable and efficient for solving highly complex optimization problems. The experimental result of QEABC-ANN has also been compared with other optimization and classiﬁcation algorithms. In all performance metric ANN with QEABC outperforms all other algorithms in terms of accuracy.