Predicting Crop yield and Effective use of Fertilizers using Machine Learning Techniques
Hemasai Katuru1, S. Ravi Kishan2, Suresh Babu Dasari3
1K. Hemasai Katuru, CSE, V R Siddhartha Engineering College, Vijayawada, India.
2S. Ravi Kishan, Computer Science and Engineering, VRSEC, Vijayawada, India.
3Suresh Babu Dasari, Computer Science and Engineering, VRSEC, Vijayawada, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 01, 2020. | Manuscript published on May 10, 2020. | PP: 1288-1292 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5911059720/2020©BEIESP | DOI: 10.35940/ijitee.G5911.059720
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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: In-season crop yield estimation has various applications such as the farmer taking corrective measures to increase the yield. We are exploring the efficient use of fertilizers. The various data mining techniques are used on data for environment. The data is related to humidity, PH. value, water, soil type and atmospheric pressure these are responsible for crop yield. This result is obtained by this algorithm are useful for farmers to take decisions about further implantation of crop yield. Crop selection method is widely used to build decision tree to overcome many problems in real time real world. One of the most important fields is decision tree. By analyzing the soil and atmosphere at particular region best crop in order to have more crop yield and the net crop yield can be predicted. This prediction will help the farmers to choose appropriate crops for their farm according to the soil type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer and months. This prediction can be carried out using Random Forest classification machine learning algorithm.
Keywords: Crop yield, Fertilizers, Humidity, Machine Learning Techniques, PH, Soil.