A Crop Recommendation System to Improve Crop Productivity using Ensemble Technique 
Shikha Ujjainia1, Pratima Gautam2, S. Veenadhari3
1Shikha Ujjainia*, Computer Science and Engineering, Rabindranath Tagore University, Bhopal, India.
2Pratima Gautam, Computer Science and Information Technology, Rabindranath Tagore University, Bhopal, India.
3S. Veenadhari, Computer Science and Engineering, Rabindranath Tagore University, Bhopal, India.
Manuscript received on January 30, 2021. | Revised Manuscript received on January 03, 2021. | Manuscript published on February 28, 2021. | PP: 102-105 | Volume-10 Issue-4, February 2021 | Retrieval Number: 100.1/ijitee.D85070210421| DOI: 10.35940/ijitee.D8507.0210421
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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: An integration of technology with crop yielding prediction methodology brought a major transformation in the production level globally. Machine learning concept has boosted that technology in such a manner that has further optimized the situation of farmer and agricultural industry. The combination of different types of algorithm enhances the competency of technological device to a level where the prediction becomes very effective and least deviation can be expected from the agricultural industry in the production level. The research of machine learning states about the integration of three types of models which is usually followed separately in programming the device. The study has proved the intervention of Information technology in the agricultural industry via different functions. An effective prediction by using the ensemble algorithm makes the agricultural industry competent enough to maintain the expected amount of production of crop.
Keywords: Machine learning, Ensemble algorithm, Integration, Agricultural Industry, Crop Prediction.