Open Access Open Access  Restricted Access Subscription or Fee Access

Innovative Approach for Sugarcane Plantation

Abhiram sanjay patil, Shripad Bhatlavande

Abstract


Sugarcane planting with traditional methods is costly, time consuming, it needs great human force,more numbers of sugarcane stalk. Also because of unskilled labor stressed or diseased bud may get
planted. To solve this problem we designed algorithm for atomization of sugarcane planting machineusing image processing. It detects the nodes efficiently and locates the cut point for stalk cutting. It
reject to cut the stalk if sugarcane suffered by Stress or disease like Pokkehha Boeng, Red Rot. In thisresearch paper we are using the Sobel and horizontal mask for edge detection for node identification
and vertical mask for to identify the crack on the stalk if any. The success ratof normal nodeidentification in sugarcane stalk by mentioned algorithm is 100% and the defective node identification
is 93.3%.


Full Text:

PDF

References


K. Moshashai, M. Almasi, S Minaei and A. M. Borghei, “Identification of Sugarcane Nodes using Image Processing and Machine Vision Technology”, International Journal of Agricultural Research, pp. 357-364, 2008.

T.R. Sinclair, R. A. Gilbert at el., “Volume of individual internodes of sugarcane stalks”, Elsevier, 2005,pg 207-215.

Mikko Hautala, Mikko Hakojarvi, “Plant growth models for precision agriculture”, Research paper, University of Helsinki, Finland, 2010.

Sungkur R., Baichoo S. at el., “An automated system to recognize Fungi-caused diseases of sugarcane leaves, “Research journal of University of Maurititus, 2009 pg.1-20.

Muhammad Asif, Samreen Amir at el., “A vision system for autonomous weed detection robot”, International journal of computer and electrical engineering, 2010, pp.1793-1797.

K.H. Ghazali, M.M. Mustafa at el., “Machine vision system for automatic weed strategy in oil palm plantation using image filtering technique, “Journal of advanced technology,2008,pp.329-339.

Cheryl Mc Carthy at el., “A preliminary field evolution of an automated vision based plant geometry measurement system”, University journal, University of Sothern Queensland, Australia, 2005, pp.19.1 -19.3.

M.B.Lak, S. Minael at el., “Apple fruit recognition under natural luminance using machine vision”, Advance journal of food science and technology, 2010, 325-327.

Mamta Juneja, Parvinder Singh Sandhu, “Image segmentation based quality analysis of agricultural products using emboss filter and Hough Transform in spatial domain”, Researcher science publication, 2009, 62-68.

Nur Badarish Ahmad Mustafa at el., “Image processing of an agriculture produce: Determination of size and ripeness of a banana”, IEEE,2008,2328-2333.

Youchan Ding, Du Chem. at el., “The mature wheat cut and uncut edge detection method based on wavelet image filtering technique”, Advance technology,2008,pp.329-339.

http:/agropidia.iitk.ac.in, Seed selection and treatment: sugarcane, 2010.pp.1-3.

Dr Rajula Shanthy, Dr. R, Thiagorajan, “Sugarcane bud chips for seed multiplication”, Sugarcane breeding institute, Indian council of agricultural research, Coimbatore, 2007.

K. Moshashai, M. Almasi at el., “Identification of sugarcane node using image processing and machine vision technology”. International journal of agricultural research, 2008, pp.357-364

Rafel C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using MATLAB, “Fourth Impression, 2008.

Shen Weizheng, Wu Yachun at el., “Grading Method of Leaf Spot Disease Based on Image Processing,” IEEE, pp.491-494, 2008.

Rui Yang, Jie Li at el., “Gradiaent-Based method for the identification of multi-node in sugarcane”, Elsevier, Information processing in Agricultural, pp.491-499, 2020.

Liu Jiaodi, Wang Mingrning at el., “Identification of Sugarcane bud based on Image processing and BP Neural network”, Proceeding of International conference on Big Data and Artificial Intelligence, pp. 466-470, April 2020.


Refbacks

  • There are currently no refbacks.