Smart Image Analysis Based Agri-Advisory System for Rice Crops

B. Sridhar*, K.B. Shirisha**, K.Puja***, M.Vivek****
* Professor, Department of Electronics and Communication Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, India.
**-**** UG Students, Department of Electronics and Communication Engineering, Lendi Institute of Engineering and Technology, Vizianagaram, India.
Periodicity:January - March'2018


Image segmentation operations are partitioning a digital image into multiple sub images called segments. In current years, Image segmentation based advanced technological solutions are developing on Agri-advisory systems, wireless sensor network based solutions. That helps in many challenging agriculture related, grading of crops, advisory systems, yield prediction, disease prediction and detection, automatic harvesting and storage. In a similar spirit, in this paper, proposed an Agri-advisory system developed for analysis of agricultural images, particularly rice crop images, developed simple and efficient methods for enhancing the resolution of images and to automatically segment defects on rice crop in their images. A super resolution method included that focuses on simple parts of construction of dictionaries consists a local luminance variance information, selection of parts of patches and reconstruction of patches. The image segmentation algorithm is a combination of chrominance thresholding and morphological operations. Our experimental results will demonstrate that such simple and efficient methods suitable for network based applications, are also quite effective, given a specific application domain.


Agri-advisory System, Color Adaptive Image Segmentation, Morphological Operations, Super Resolution Techniques.

How to Cite this Article?

Sridhar, B., Shirisha, K, B., Puja, K., and Vivek, M.(2018). Smart Image Analysis Based Agri-Advisory System for Rice Crops. i-manager's Journal on Software Engineering, 12(3), 27-37.


[1]. Ariana, D., Guyer, D. E., & Shrestha, B. (2006). Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture, 50(2), 148-161.
[2]. Dubey, S. R., & Jalal, A. S. (2012, November). Detection and classification of apple fruit diseases using complete local binary patterns. In Computer and Communication Technology (ICCCT), 2012 Third International Conference on (pp. 346-351). IEEE.
[3]. Garrido-Novell, C., Pérez-Marin, D., Amigo, J. M., Fernández-Novales, J., Guerrero, J. E., & Garrido-Varo, A. (2012). Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras. Journal of Food Engineering, 113(2), 281-288.
[4]. Gonzalez, R. C., & Woods, R. E. (2008). Digital Image rd Processing, 3 Ed. Pearson.
[5]. Hariharan, G. T., Hariharan, G. P. S., & Anandh, V, R. (2016). Crop disease identification using image processing. International Journal of Latest Trends in Engineering & Technology, 6(4), 20-40.
[ 6 ] . Hawkson, E . E . , & N g n e n b , T. ( 2 0 1 5 ) . Retrieved from http://www.graphic. yields-to-pests-diseases.html
[7]. Jhuria, M., Kumar, A., & Borse, R. (2013, December). Image processing for smart farming: Detection of disease and fruit grading. In Image Information Processing (ICIIP), 2013 IEEE Second International Conference on (pp. 521- 526). IEEE.
[8]. Khairnar, K., & Dagade, R. (2014). Disease detection and diagnosis on plant using image processing - A Review. International Journal of Computer Applications, 108(13), 36-38.
[9]. Leemans, V., & Destain, M. F. (2004). A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering, 61(1), 83-89.
[10]. Mukherjee, M., Pal, T., & Samanta, D. (2012). Damaged paddy leaf detection using image processing. International Journal of Global Research in Computer Science, 3(10), 07-10.
[11]. Nti, I. K., Eric, G., & Jonas, Y. S. (2017). Detection of plant leaf disease employing image processing and gaussian Smoothing Approach. International Journal of Computer Applications, 162(2), 20-25.
[12]. Phadikar, S., & Sil, J. (2008, December). Rice disease identification using pattern recognition techniques. In Computer and Information Technology, 2008. ICCIT th 2008. 11 International Conference on (pp. 420-423). IEEE.
[13]. Prasad, S., Peddoju, S. K., & Ghosh, D. (2014, April). Energy efficient mobile vision system for plant leaf disease identification. In Wireless Communications and Networking Conference (WCNC), 2014 IEEE (pp. 3314- 3319). IEEE.
[14]. Shete, S., Gonsalves, T., & Jalihal, D. (2016, March). Image analysis for network based Agri Advisory System. In Communication (NCC), 2016 Twenty Second National Conference on (pp. 1-6). IEEE.
[15]. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41-49.
[16]. Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. F., & Debeir, O. (2011). Automatic grading of Bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 75(1), 204- 212.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.