Home > Archives > IJSRST173423
Authors(2) :-Harsha Madhavdas Chirmade, Mayuri Gachake
Agriculture is very labour intensive field and only field where the robots are not involved. Now-a- days many Industries are trying to reduce this human labour by making robots and machines. A vision based row guidance Method is presented to guide a robot platform which is designed independently to drive through the row crops in a field according to the design concept of open architecture. Then, the offset and heading angle of the robot Platform are detected in real time to guide the platform on the basis of recognition of a crop row using machine Vision. And the control scheme of the platform is proposed to carry out row guidance. Here we are designing a autonomous intelligent farming robot which indicates the plant health by observing the colour of their leaves and based on the height of the plant. The robot also notes the surrounding environmental conditions of the plant like Temperature, humidity so that the robot will decide about health of plat and will display on the LCD. The robot has also watering mechanism it will water the plants according to their needs by observing temperature and humidity. It will also tell when the cutting process should take place by observing the leaf colour.
Harsha Madhavdas Chirmade, Mayuri Gachake
ARM7, Image processing, Irrigation Robot
- Laudien, R., Bareth, G. & Doluschitz, R., 2004b: “Comparison of remote sensing based analysis of crop diseases by using high resolution multispectral and hyperspectral data - case study: Rhizoctonia solani in sugar beet” in Proceedings of the 12th International Conference on Geoinformatics, June 7th -9th, Gavle, p.670-676/.
- Pierre Sibiry Traore, “The view from above” in ICT Update, a remote sensing scientist and GIS head at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 23 February 2010.
- Prasad Babu, M. S. and Srinivasa Rao , B. "Leaves recognition using back-propagation neural network - advice for pest and disease control on crops", Technical report, Department of Computer Science & Systems Engineering, Andhra University, India.
- Camargo, A. and Smith, J. S., "An imageprocessing based algorithm to automatically identify plant disease visual symptoms, Biosystems Engineering", Volume 102, Issue 1, January 2009, Pages 9-21, ISSN 1537-5110.
- Hillnhuetter, C. and A.-K. Mahlein, "Early detection and
- localisation of sugar beet diseases: new approaches", Gesunde Pfianzen 60 (4) (2008), pp. 143-149.
- ] Rumpf, T., A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, L. Plumer, "Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance", Computers and Electronics in Agriculture, Volume 74, Issue 1, October 2010, Pages 91-99, ISSN 0168-1699.
- Al-Bashish, D., M. Braik and S. Bani-Ahmad."Detection and classification of leaf diseases using Kmeans- based segmentation and neural-networks-based classification". Inform. Technol. J., 10: 267-275.
Published in : Volume 3 | Issue 4 | May-June 2017
Date of Publication : 2017-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 172-175
Manuscript Number : IJSRST173423
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Harsha Madhavdas Chirmade, Mayuri Gachake, "Farming Robot", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 4, pp.172-175, May-June-2017
URL : http://ijsrst.com/IJSRST173423