Page 545 - e-Book

6. Image restoration-The process of getting the original and pure image from noisy and corrupt

image
7. Image segmentation-Image is segmented into no of regions which leads to show different

objects
8. Feature Extraction-Image characteristics are identified by applying this process on image

dataset.
9. Object recognition-Finding and identifying objects in an image or video sequence.

2.2 characteristics of agriculture image:-

According to the characteristics of agriculture image, new image recognition scheme and

image authentication scheme were designed, which based on perceptual hash algorithm.
Some tomato leaf diseases pictures were used to achieve image perceptual hash feature

extraction. And the experimental results show that the same diseases images have closer
perceptual hash characteristics[3].An early detection of rice plant disease especially rice plant

leaves disease detection can assist farmers to take necessary precaution at the early stage and
can achieve better quality of crops. Rice plant can be affected by various types of fungal

infectious diseases and among them rice blast is a common one. There are a numerous image

processing approaches available today which can analyze rice plant leaves disease. Existing
most approaches considered binary threshold based segmentation approach although input

images are always RGB color images. To develop an automated system to identify and

classify rice blast diseases it is always beneficial to use RGB color images as input and to
provide analysis results in RGB color images as well. This study proposed a suitable frame

work where enhancement, filter, color segmentation and color feature for classification steps
were incorporated for identification. CNN classifier was applied to increase the identified

accuracy rate[4].In order to overcome the inherent challenges in the recognition of weeds in
wheat fields, we accomplished the following in this study:1) A fast recognition method for

weeds in wheat fields combining RGB images and depth images was proposed. For the first

time, RGB-D fusion information was applied to the classification of weeds in wheat fields.2)
Focusing on the issue of holes in depth images, a depth information repair method based on

RGB image information guidance was proposed that utilizes the object consistency of RGB
and depth image acquisition.3) A more robust classification algorithm of the various weed

species in wheat fields was proposed. The different features of weeds in wheat fields at the
peak of weed emergence were analyzed. Color, position, texture, and depth information was

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