Review of Leaf Unhealthy Region Detection Using Image Processing Techniques

Author:

A. Dhole S.,Shaikh Rukaiyya Pyarelal

Abstract

Abstract- In agricultural  field the plants comes to an attack from the various pets bacterial and micro-organism diseases. This diseases  attacks on the plant leaves, steams, and fruit part. This present review paper discussed the image processing techniques which is used in performing the early detection of plant diseases through leaf feature inspection. the basic objective of this work is to develop image analysis and classification techniques for extraction and finally classified the diseases present on leaf. Image of leaf is captured  and the process is performed and to determine the status of each plant. Here proposed model  divide into four parts. The  image preprocessing including normalization and contrast adjustment; segment the region of interest  determine by using color transform YCbCr and bi-level thresholding for statistical usage to determine the defect and severity area of plant leaves. The texture feature extraction using statistical GLCM (Gray Level Co-occurrences Matrix)  and color feature by means values.[1] Finally classification achieved using random markov model.

Publisher

Institute of Advanced Engineering and Science

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Instrumentation,Information Systems,Control and Systems Engineering,Computer Science (miscellaneous)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated Plant Health Assessment Through Detection of Diseased Leaflets;Lecture Notes in Electrical Engineering;2023

2. Smart Plant Health Monitoring and Leaf Disease Detection;Computational Vision and Bio-Inspired Computing;2023

3. Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques;Mathematical Problems in Engineering;2022-01-31

4. Image Processing Techniques Aiding Smart Agriculture;Modern Techniques for Agricultural Disease Management and Crop Yield Prediction;2020

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