Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Machine Vision Based Agricultural Weed Detection and Smart Herbicide Spraying


Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, India
 

Objectives: To detect and remove the unwanted weeds present in an agricultural field and spray the pesticide at exact location of plants. Methods/Statistical Analysis: The entire process is implemented by using Labview programming with Machine Vision libraries. The movable agricultural vehicle is incorporated with machine vision library and microcontroller. The unwanted plants or weeds are identified by using Labview simulation process. Plants must be differentiated accordingly based on the type of weed that will affect the yield. The characteristics of weed should be properly studied. Findings: The unwanted plants or weeds grown will reduce the yield. Weed detection and removal in agricultural field is the main process. Utilization of the herbicides can be diminished just by appropriate breaking down of the weed. Application/Improvements: The position of weeds is automatically identified. Spraying of herbicide can be taken automatically or takes place once the position of the weed is detected by using spraying module.
User

  • Aware AA, Joshi K. Crop and weed detection based on texture and size features and automatic spraying of herbicides. International Journal of Advanced Research in Computer Science and Software Engineering. 2016; 6(1):1–7.

  • Malemath VS, Hugar SM. A new approach for weed detection in agriculture using image processing techniques. International Journal of Advanced Scientific and Technical Research. 2016; 6(3):356–9.

  • Bhanumathi G, Subhakar B. Smart herbicide sprayer robot for agriculture fields. International Journal of Computer Science and Mobile Computing. 2015; 4(7):571–4.

  • Priyadharsini S, Sathiskumar BS. Developing a real time smart herbicide sprayer robot and automatic weed detection system. International Journal of Emerging Technology and Advanced Engineering. 2015; 5.

  • Shapira U, Herrmann I, Karnieli A, Bonfil JD. Weeds detection by ground level hyper spectral data. International Society for Photogrammetry and Remote Sensing. 201; 8(4):27–33.

  • Jones G, Truchetet F. Modeling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance. Precision Agriculture. 2009; 10(1):1–15. Crossref.

  • Piron A, Leemans V, Kleynen O, Lebeau F, Destain MF. Selection of the most efficient wavelength bands for discriminating weeds from crop. Computers and Electronics in Agriculture. 2008; 62:141–8. Crossref.

  • Sanchez A, Marchant A. Fusing 3D information forcrop/ weeds classification. Proceedings of the 15th International Conference on Pattern Recognition (ICPR'OO). 2000; 4:4295–99.

  • Samseemoung G, Soni P, Jayasuriya PWH, Salokhe MV. Application of Low Altitude Remote Sensing (LARS) platform for monitoring crop growth and weed infestation in a soyabean plantation. Precision Agriculture. 2012; 13(6):611–27. Crossref.

  • Xavier P, Burgos, Angela R, Alberto T, Gonzalo P, Cesar F. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Computers and Electronics in Agriculture. 2009; 65:176–85. Crossref.


Abstract Views: 124

PDF Views: 0




  • Machine Vision Based Agricultural Weed Detection and Smart Herbicide Spraying

Abstract Views: 124  |  PDF Views: 0

Authors

S. Mohan Raj
Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, India
V. Kavitha
Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur – 639113, Tamil Nadu, India

Abstract


Objectives: To detect and remove the unwanted weeds present in an agricultural field and spray the pesticide at exact location of plants. Methods/Statistical Analysis: The entire process is implemented by using Labview programming with Machine Vision libraries. The movable agricultural vehicle is incorporated with machine vision library and microcontroller. The unwanted plants or weeds are identified by using Labview simulation process. Plants must be differentiated accordingly based on the type of weed that will affect the yield. The characteristics of weed should be properly studied. Findings: The unwanted plants or weeds grown will reduce the yield. Weed detection and removal in agricultural field is the main process. Utilization of the herbicides can be diminished just by appropriate breaking down of the weed. Application/Improvements: The position of weeds is automatically identified. Spraying of herbicide can be taken automatically or takes place once the position of the weed is detected by using spraying module.

References





DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i23%2F129124