Authors

Shruti Saini, Sugandh Raghav
Department of Physical Sciences, Banasthali Vidyapith, Rajasthan

Dr. Shivani Saxena
Assistant Professor, Department of Physical Sciences, Banasthali Vidyapith, Rajasthan

Abstract

The proposed work analyzes models trained using features extracted from RGB and edge detection methods for classification of plant leaf diseases. A dataset of various plant leaves was processed using MATLAB for simulation and feature extraction, with models trained using deep learning techniques. Accuracy and efficiency were assessed using confusion matrix and ROC curve.

Keywords

Confusion Matrix, Classification of Plant Leaf Diseases, Edge Detection Model, RGB Model, ROC Curve

Conclusion

Since plants are a major food source, early disease detection is crucial in agriculture. Experimental results indicate that edge detection models are more accurate for plants with sharp edges, while RGB models effectively classify various leaf types.