Electronic Monitoring and Disease Diagnosis of Oryza Sativa Crops through an IoT Enabled Embedded System
R. Menaka1, Keshav G2, Chandra Karan R3, Imtiaz Ahamed4, Alamelu Nachiappan5, Karthik. R6
1R. Menaka, School of Electronics Engineering, VIT Chennai Campus, Chennai, India.
2Keshav G, School of Electronics Engineering, VIT Chennai Campus, Chennai, India.
3Chandra Karan R, School of Electronics Engineering, VIT Chennai Campus, Chennai, India.
4Imtiaz Ahamed, School of Electronics Engineering, VIT Chennai Campus, Chennai, India.
5Alamelu Nachippan, Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry
6Karthik. R, School of Electronics Engineering, VIT Chennai Campus, Chennai, India.
Manuscript received on 12 April 2019 | Revised Manuscript received on 17 May 2019 | Manuscript published on 30 May 2019 | PP: 191-197| Volume-8 Issue-1, May 2019 | Retrieval Number: A3006058119/19©BEIESP
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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: Rice is one of the most important crops in human history and is a staple diet of a majority of the human population at any given point in time. It is therefore of utmost importance to maximize the yield of every harvest of rice for a multitude of reasons, from increasing profit margins to reducing world hunger. With the current advancements in technology it is possible to monitor the growth and progress of a crop to a level not feasible before. This work is an attempt to create a fully self-sufficient, IOT enabled embedded system to automate the processes related to rice crop farming. The environmental condition parameters like water, humidity, soil moisture, spread of diseases can be conveyed to a farmer. The system is based on intelligent sensing, thereby reducing the power consumption and the need to frequently replace sensors. The data recorded enables the farmer to make informed decisions for crop maintenance from a remote location at a low cost.
Index Terms: Raspberry Pi, Oryza Sativa, Magnaporthegrisea, Rice blast, OpenCV
Scope of the Article: Design and Diagnosis