Agrocompanion: A Smart Farming Approach Based on Iot and Machine Learning
Rashi Kaur1, Kodali Havish2, Thirumala Kaustub Dutt3, Gangidi Manohar Reddy4
1Ms. Rashi Kaur, Computer Science & Engineering at Mahatma Gandhi Institute of Technology, Hyderabad, India.
2Mr. Havish Kodali,a Computer Science & Engineering at Mahatma Gandhi Institute of Technology, Hyderabad, India.
3Mr. Thirumala Kaustub Dutt, Computer Science & Engineering at Mahatma Gandhi Institute of Technology, Hyderabad, India.
4Mr. Gangidi Manohar Reddy, Computer Science & Engineering at Mahatma Gandhi Institute of Technology, Hyderabad, India.
Manuscript received on September 10, 2020. | Revised Manuscript received on September 30, 2020. | Manuscript published on October 10, 2020. | PP: 254-262 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.L79841091220 | DOI: 10.35940/ijitee.L7984.1091220
Open Access | Ethics and Policies | Cite | Mendeley
© 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 one of the cardinal sectors of the Indian Economy. The proposed system offers a methodology to efficiently monitor and control various attributes that affect crop growth and production. The system also uses machine learning along with the Internet of Things (IoT) to predict the crop yield. Various weather conditions such as temperature, humidity, and soil moisture are monitored in real-time using IoT sensors. IoT is also used to regulate the water level in the water tanks, which helps in reducing the wastage of water resources. A machine learning model is developed to predict the yield of the crop based on parameters taken from these sensors. The model uses Random Forest Regressor and gives an accuracy of 87.5%. Such a system provides a simple and efficient way to maintain and monitor the health of the crop.
Keywords: Agriculture, IoT, Sensors, Machine Learning, Random Forest Regressor.