Predicting Tulip Leaf Diseases: A Integrated CNN and Random Forest Approach
Deepak Banerjee, Vinay Kukreja, Shanmugasundaram Hariharan, Vishal Jain, Varun Jindal
Abstract
In this paper, the methods of Random Forest (RF) & Convolutional Neural Network (CNN) algorithms are combined to present a novel approach for classifying bacterial infections in plant species. The proposed model extracts characteristics from the input photos using three layers of convolution, three layers that max-pool features, and a layer that is completely connected before categorizing the images using RF into various bacterial disease classifications. Recall, F1-Score, support, and accuracy measures were used to evaluate the model’s performance using a dataset of plant image data that included seven different types of bacterial illnesses. The experimental results demonstrate that the proposed CNN-RF methodology provides remarkable precision in categorizing the images when compared to other state-of-the-art techniques. The model specifically achieves an average weighted recall of 81.54%, an F1-Score of 81.54%, and an accuracy of 81.54%. The recommended approach is effective at identifying various bacterial illnesses in plants because the model outperforms rival models when it comes to of macro & micro-average F1-Scores. The recommended method for diagnosing bacterial diseases in plants makes use of machine learning as well as deep learning techniques. Because bacterial illnesses in plants can have a substantial influence on agricultural production, a model’s high precision, and efficacy can help identify and stop the spread of these infections.