Monitoring and Controlling Various Factors for Maize Cultivation using IOT and ML
Arun V1, Oandrilla Podder2, Tejal Arya3, Pratyaksh Sharma4
1Arun V, Assistant Professor, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
2Oandrilla Podder, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
3Tejal Arya, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
4Pratyaksh Sharma, Department of Computer Science Engineering SRM Institute of Science & Technology Chennai, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1881-1886 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1009109119/2019©BEIESP | DOI: 10.35940/ijeat.A1009.109119
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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: Sensors are global devices that are frequently used to detect and respond to electrical or optical signals. The sensors convert the physical variables. Generally they are to sense all materials, have fast response time, are cost effective and are quite predictable. However there are distance limitations, requires physical contact with target and are quite sensitive to extreme environmental changes. Monitoring and controlling environmental factors is a major factor to improve the yield of maize, which is our area of concern. The system includes monitoring temperature, humidity, water level, pH and the level of chemicals present. A single Raspberry-pi board is programmed to sense and monitor the system. The sensed values of the sensors are viewed on a LCD display. The critical and the desired ranges are fed as a database in excel sheet. Looking on to the results obtained from the sensors, the values in the database, and putting Logistics Regression Algorithm to use, smart predictions are done to display the information required to the farmer for efficient cultivation of the crop. Considerably, by the connections through multi-core processor, the system deliberately connects and improves the network among the sensors, which are connected to the processor to get better data transmission. An increase in product quality and quantity is achieved by following the above mentioned system.
Keywords: Smart farming, raspberry pi, IoT, logistic regression.