An Image-Based Automated Model for Plant Disease Detection Using Wavelet

The popularity of using automated models in every sector is increasing day by day. Developing an automated model to recognize various diseases in plants from leaf images is the main focus of this research study. Various diseases can occur in plants during their entire lifetime. Automated identification saves time and eliminates human intervention. This study uses image segmentation to separate affected and unaffected regions from leaf images. The discrete wavelet transform has been used to take out significant patterns from images. Local binary pattern has also been used as a texture feature descriptor. The study shows a significant improvement in accuracy using these feature combinations. To train and test the model, a benchmark data set has been used. The efficiency of our model outperforms state-of-the-art models in comparison. The efficiency of our model is 95.08%.