Abstract
Crop diseases pose a significant threat to food security, but their timely detection is challenging in many regions due to a lack of necessary infrastructure. Recent advancements in leaf-based image classification have yielded promising outcomes. This study leverages Random Forest to differentiate between healthy and diseased leaves using newly created datasets. The proposed methodology includes dataset generation, feature extraction, classifier training, and image classification. Diseased and healthy leaf datasets are collectively trained using Random Forest for accurate classification. Feature extraction techniques are utilized for this purpose to enhance the accuracy of identification. Key Words: Leaf Based Classification, Disease Prediction, Feature Extraction, Random Forest, Diseased Leaves
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
