Automatic Agriculture Spraying Robot with Smart Decision Making

  • S. Sharma
  • R. Borse
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 530)


The responsibility of controlling and managing the plant growth from early stage to mature harvest stage involves monitoring and identification of plant diseases, controlled irrigation and controlled use of fertilizers and pesticides. The proposed work explores the technology of wireless sensors for remote real time monitoring of vital farm parameters like humidity, environmental temperature and moisture content of the soil. We also employ the technique of image processing for vision based automatic disease detection on plant leaves. Thus this paper vigorously describes the design and construction of an autonomous mobile robot featuring plant disease detection, growth monitoring and spraying mechanism for pesticide, fertilizer and water to apply in agriculture or plant nursery. To realize this work we provide a compact, portable and a well founded platform that can survey the farmland automatically and also can identify disease and can examine the growth of the plant and accordingly spray pesticide, fertilizer and water to the plant. This approach will help farmers make right decisions by providing real-time information about the plant and it’s environment using fundamental principles of Internet, Sensor’s technology and Image processing.


Disease Detection Autonomous Mobile Robot Soil Moisture Sensor Spray Pesticide Virtual Instrumentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    A. Camargoa, J.S. Smith. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. elsevier journal of computer and electronics in agriculture. doi:10.1016/j.biosystemseng.2008.09.030Google Scholar
  2. 2.
    H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z, AL Rahamneh. (2011). Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications. Volume 17. doi:10.5120/2183-2754.Google Scholar
  3. 3.
    Joaquin Gutierrez, Juan Francisco, Villa-Medina, Alejandra Nieto- Garibay, and Miguel Angel Porta-Gandara. (2014). Automated Irrigation System Using a Wireless Sensor Network and GPRS Module. IEEE Transactions On Instrumentation And Measurement, Vol. 63.doi:10.1109/TIM.2013.2276487.Google Scholar
  4. 4.
    K. Prema N. Senthil Kumar, S.S. Dash, Sudhakar Chowdar. (2012). Online control of remote operated agricultural Robot using Fuzzy Controller and Virtual Instrumentation. IEEE-International Conference On Advances In Engineering, Science And Management (lCAESM):196 – 201.Google Scholar
  5. 5.
    Mohamed Rawidean Mohd Kassim, Ibrahim Mat, Ahmad Nizar Harun. Wireless Sensor Network in Precision Agriculture Application. (2014). IEEE conference MIMOS, Malaysia. doi:10.1109/CITS.2014.6878963.Google Scholar
  6. 6.
    Naiqian Zhang, Maohua Wang, Ning Wang. (2002). Precision agriculture-a worldwide overview. Computers and Electronics in Agriculture elsevier, volume 36:113-132, doi:10.1016/S0168-1699(02)00096-0.Google Scholar
  7. 7.
    Peng Jian-sheng. (2014). An Intelligent Robot System for Spraying Pesticides. The open electrical and electronic Engineering Journal,8 : 435-444.Google Scholar
  8. 8.
    R. Pydipati, T.F. Burks, W.S. Lee. (2006). Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2006.01.004.Google Scholar
  9. 9.
    Sai Kirthi Pilli, Bharathiraja Nallathambi, Smith Jessy George, Vivek Diwanji. (2015). eAGROBOT- A robot for early crop disease detection using image processing. IEEE Sponsored 2nd International Conference On Electronics And Communication System (ICECS). doi:10.1109/ECS.2014.7090754.Google Scholar
  10. 10.
    Snehal M. Deshmukh, Dr. S.R.Gengaje. (2015). ARM- based pesticide spraying robot. International Journal of Engineering Research and General Science. Volume 3: ISSN 2091-2730Google Scholar
  11. 11.
    Tao Liu, Bin Zhang, Jixing Jia. (2011). Electromagnetic navigation system design of the green house spraying robot. IEEE second international conference (MACE), Hohhot.doi:10.1109/MACE.2011.5987400.Google Scholar
  12. 12.
    Van Li Chunlei, Xia Jangmyung Lee. (2015). Vision-based pest detection and automatic spray of greenhouse plant. International conference on (ISIE), Korea.doi: 10.1109/ISIE.2009.5218251.Google Scholar
  13. 13.
    Weizheng, S., Yachun, W., Zhanliang, C., and Hongda (2008).Grading method of leaf spot disease based on image processing. International Conference on Computer Science and Software Engineering:12–14.doi:10.1109/CSSE.2008.1649Google Scholar
  14. 14.
    Zhang Junxiong, Cao Zhengyong, Geng Chuangxing (2009). Research on Precision Target spray robot in greenhouse. Transactions of the Chinese Society of Agricultural Engineering. Volume 25:70-73.Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • S. Sharma
    • 1
  • R. Borse
    • 1
  1. 1.Sinhgad Academy of EngineeringPuneIndia

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