Plant Leaf Detection by Multi-Class SVM Model Using CCM and Histogram Features
Volume: 13 - Issue: 06 - Date: 01-06-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU390 | Page No.: 139-149
Author: Sandeep Chouhan
Co- Author: prof. Nitya khare ,Hod,Dr. Sneha soni
Abstract:-Agriculture,
serving as the cornerstone of civilizations, plays a pivotal role in providing sustenance
and essential resources. Given its paramount significance in human life as a
primary source of food, the detection of plant diseases has emerged as a
critical concern. Although traditional methods exist for identifying such
diseases, agriculture professionals and plant pathologists have traditionally
relied on visual inspection alone to detect leaf diseases. However, this
conventional approach to identifying plant leaf diseases can be subjective,
time-intensive, financially burdensome, and necessitates a substantial
workforce equipped with extensive knowledge about various plant diseases. This
paper has proposed a model that classify the plant unhealthy leaf and identify
the type of infected image. Paper has found that proposed model has increases
the work detection accuracy by the use of Co-occurrence matrix and histogram
feature. Extracted features were used for the training of multi class support
vector machine. Experiment was done on real tomato plant leaf dataset and
result shows that proposed model has increases the detection accuracy of multi
class leaf diseases as well.
Key Words:-Image Processing, Plant Leaf, Feature Extraction, Image Classification.
Area:-Engineering
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