Amalgamation of Machine Learning Algorithms for Crop Yield Prediction
D.Maghesh Kumar1, K.Mohan Kumar2
1D.Maghesh Kumar*, Research Scholar pursuing Ph.D., in the PG and Research Department of Computer Science, Rajah Serfoji Government College, Thanjavur, Affiliated to Bharathidasan University, Trichirappalli, Tamil Nadu, India.
2Dr. K. Mohan Kumar, Heading the PG and Research Department of Computer Science, Rajah Serfoji Government College, Thanjavur, Affiliated to Bharathidasan University, Trichirappalli, Tamil Nadu, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 02, 2020. | Manuscript published on April 10, 2020. | PP: 1320-1324 | Volume-9 Issue-6, April 2020. | Retrieval Number: E3004039520/2020©BEIESP | DOI: 10.35940/ijitee.E3004.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture is India’s prime occupation. In Indian economy agriculture plays a major role by means of providing more employment opportunities for the people. In order to provide food to the huge population of India, agriculture sector needs to maximize its crop productivity. This research work presents an approach which uses different Machine learning (ML) techniques by considering the various parameters of cultivated crop to predict the best yield. Further in this Multiple Linear Regression (MLR) technique and artificial neural networks (ANN) are used to make a brief analysis for the prediction crop yield. With the above idea a new model was created, and from this numerical results were obtained. The accuracy and efficiency of the method has been explored and results from the artificial neural network and regression methods were obtained and compared. Agriculture is India’s prime occupation. In Indian economy agriculture plays a major role by means of providing more employment opportunities for the people. In order to provide food to the huge population of India, agriculture sector needs to maximize its crop productivity. This research work presents an approach which uses different Machine learning (ML) techniques by considering the various parameters of cultivated crop to predict the best yield. Further in this Multiple Linear Regression (MLR) technique and artificial neural networks (ANN) are used to make a brief analysis for the prediction crop yield. With the above idea a new model was created, and from this numerical results were obtained. The accuracy and efficiency of the method has been explored and results from the artificial neural network and regression methods were obtained and compared.
Keywords: Agriculture, Artificial Neural Networks, Crop Yield, Machine Learning, Multiple Linear regression.
Scope of the Article: Artificial Intelligence and machine learning