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
DOI : https://doi.org/10.26634/jse.12.3.14087

Abstract

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.

Keywords

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. https://doi.org/10.26634/jse.12.3.14087

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