Identification of healthy biological leafs using hybrid-feature classifier

ABSTRACT

Plants are an important element of the ecosystem that helps in controlling carbon emissions and environmental changes. Characterization and identification are a need for protecting plants and for people to understand plant protection. Plant leaves are the main parts for detection. Characterizing leaves now has been a significant and complicated task, particularly with the features of leaves. Leaf images of two different types are considered here, one is healthy while the other is unhealthy, and divided into two distinct classes. The proposed method incorporates features of the leaf images that are extracted utilizing the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) feature extraction techniques. The outcomes are classified using three different classifiers: Random Forest, Multilayer Perceptron, and Naïve Bayes with an accuracy of 95.84%, 98.33%, and 82.89% respectively. The classifiers successfully classify the healthy and diseased leaves of various plants that were considered here. Hence as per the investigation, the study can be valuable for analysts for plant recognition, characterization, and diagnosis.

KEYWORDS:

• Leaf classification
• GLCM
• GLRLM
• random forest
• MLP
• Naive Bayes

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Vijaya Patnaik

Dr. Asit Subudhi received B.E. in Electronics & Communication in 2005 and M. Tech in Communication System Engineering from BPUT, Bhubaneswar, in 2010, and Ph.D. in Electronic Engineering at SOA deemed to be University in 2018. Presently working as an Associate Professor in the Department of Electronics & Communication Engineering, SOA University, Odisha, India. He has over 18 years of experience in teaching and his research expertise focuses on Biomedical signal and image processing, and VLSI design. He is a member of IEEE. Email: [email protected]

Monalisa Mohanty

Dr. Monalisa Mohanty received M. Tech in Communication System Engineering from BPUT, Bhubaneswar, in 2010 and Ph.D. in Electronics Engineering at SOA University in 2019. Currently working as an Associate Professor in the Electronics and Communication Engineering department, at ITER in SOA deemed to be University. She is having a total of 15 years of experience. Her area of interest is Biomedical signal and image processing research. She has also bagged publications in many reputed journals and conferences. Email: [email protected]

Asit Kumar Subudhi

Ms. Vijaya Patnaik received M. Tech in Electronics and Instrumentation Engineering from CET, Bhubaneswar, in 2020 and continuing her Ph.D. in Electronics Engineering at SOA University since 2020. Her area of interest is Biomedical signal and image processing research. She has also bagged publications in many journals and conferences. Email: [email protected]