Open AccessPest Detection using Image Processing
Author(s) -
Shilpa Itnal,
M. Akhila,
S. Khadri,
Vanukuri Meher Sreemaiee
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6875.129219
Subject(s) - productivity , pest analysis , agricultural engineering , agriculture , crop productivity , image processing , watershed , agricultural productivity , image segmentation , crop , segmentation , computer science , artificial intelligence , agroforestry , image (mathematics) , agronomy , computer vision , environmental science , biology , ecology , engineering , horticulture , economics , macroeconomics
Agriculture is one of the most significant economic activity. They are many ways that leads to the low productivity of agriculture, but the best method to protect the crop is by detecting the diseases in the early stage. In most of the cases diseases are caused by pest, insects, pathogens which reduce the productivity of the crop at the large scale. If pests are detected on the leaves then, precautions should be taken to avoid huge productivity loss at the end. The main objective of this paper is to identify the pests using image processing techniques like Gaussian blur, segmentation, watershed separation, morphological operations. These techniques are more efficient and less time consuming while identifying the pests over the leaf image with high intensity.