Crop Yield Prediction by Hybrid Technique with Crop Datasets

KUSUM LATA, Sajidullah Khan

Abstract


Agriculture is one of the intense domains across the globe which has greater impact on the development of a country.  There are various tools and techniques developed for the farmers and they are taking advantages of it. Also, the power of artificial intelligence is realized in agriculture field with the application of machine learning and deep learning algorithms. Numerous models have been proposed using the conventional algorithms, but still it is needed to improve the prediction accuracy. Therefore, in the proposed model a hybrid technique is designed by combining the Machine learning, deep learning algorithms and optimization with particle swarm optimization PSO methods to improve the prediction accuracy. In the proposed model, SVM is used as Machine leaning algorithm and RNN-LSTM is used as deep learning algorithm. The crop data sets of Maharashtra for previous years are used as input to the model and prediction will be done for the coming years. The proposed model has potential in improving the yield prediction for various crops like onion, grapes, cotton etc. produced in the Maharashtra State of India.

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References


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