Hybrid Machine Learning Approach for Plant Disease Identification

Abstract

In this study, a hybrid architecture that combines the feature-extraction capability of CNN and the classification power of RF is proposed to focus on the correct detection of plant diseases, which is Convolutional Neural Network-Random Forest (CNN-RF). Data acquisition and preprocessing, which consisted of image normalization, augmentation, and resizing to make sure that the models could fit the data and enhance generalization, started with the methodology. The CNN element was trained to automatically learn discriminative features on the plant leaf images, which were then inputted into an RF classifier which was optimized by hyperparameter optimization. The performance measurement utilized conventional measures, such as accuracy, precision, recall, and F1-score and the Receiver Operating Characteristic (ROC) curve analysis. It has been proven by experimental results that the hybrid CNN-RF model is better than the standalone CNN model and RF model. The proposed model attained an accuracy of 96.3, precision of 95.8, recall of 96.7 and F1-score of 96.2, which was better than CNN (93.5% accuracy) and RF (88.4% accuracy) baselines. The tuning of hyperparameters was demonstrated to be of great benefit to the outcomes of classification as illustrated in the tuning heat map. The hybrid model had a close Area Under the Curve (AUC) of 1.0 on the ROC curve, which is ideal sensitivity and specificity.

Code and data availability

The paper describes a hybrid CNN-RF plant disease model using Plant Village images and field-collected leaf images, but provides no authors' public dataset deposit, code release, trained model, or supplement with a URL. Plant Village is cited prior work, not a paper-specific asset, and no availability/deposit language,

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