Prediction of Crops based on Environmental Factors using IoT & Machine Learning Algorithms
Ashok Tatapudi1, P Suresh Varma2
1Ashok Tatapudi*, Assistant Professor, Department of Computer Science & Engineering, University College of Engineering, AdiKavi Nannaya University, Rajamahendravaram, India.
2P Suresh Varma, Professor, Department of Computer Science & Engineering, University College of Engineering, AdiKavi Nannaya University, Rajamahendravaram, India.
Manuscript received on October 17, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 5395-5401 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4695119119/2019©BEIESP | DOI: 10.35940/ijitee.A4695.119119
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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: India being an agricultural country, the most part of economy is depends on yield growth. Agriculture is largely dependent on rainwater and also relies on different soil variables such as aspects of nitrogen, phosphorus, potassium and climate such as temperature, precipitation, etc. The technological growth in agriculture will increase the crop productivity. Remote sensing systems like IOT systems are being more widely used in smart farming systems, these systems produce generous amount of data. Machine learning is an ongoing work that has been filed to forecast the plant based on data trends. The proposed system would integrate sensors such as Ph., Moisture, Rainfall, Temperature and Humidity to analyze the information from these sensors and to implement machine learning algorithms: Linear Regression, Decision Trees, Random Forest, and GDBoost. The most desirable crops are predicted according to the current environment. This work gives farmers a better prediction to plant what kind of crops in their farm field based on the criteria listed above in order to improve Smart Farming’s productivity.
Keywords: Agriculture, IoT, Machine Learning, Data Analytics, Prediction
Scope of the Article: Machine Learning