A novel approach for image-based olive leaf diseases classification using a deep hybrid model

Abstract

The olive tree is affected by a variety of diseases. To identify these diseases, many farmers typically use traditional methods that require a lot of effort and specialization. These methods include visually observing the tree or conducting tests in a laboratory. Fortunately, recent progress in machine learning (ML) and deep learning (DL) has demonstrated promising potential to automatically classify diseases with both high accuracy and speed. However, as indicated by the literature, only a few studies are utilizing ML and DL techniques for identifying and categorizing diseases that affect olive trees. Therefore, in this study, we collected a dataset containing 4138 images of olive leaves from various sources. The dataset comprises four categories: three representing diseases and one denoting a healthy category. We also introduced an innovative approach to classify olive leaf diseases by combining deep learning architectures, specifically convolutional neural networks (CNNs), with machine learning classifiers. In this approach, we developed a total of 30 distinct deep hybrid models (DHMs), utilizing six pre-trained convolutional neural network architectures (VGG19, ResNet50, MobileNetV2, InceptionV3, DenseNet201, and EfficientNetB0) as feature extractors, along with five machine learning classifiers (MLP, LR, RF, SVM, and DT). To assess the performance of the DHMs, we used performance evaluation metrics (Accuracy, Precision, Recall, F1-score) and we conducted an assessment to validate the reliability rating of the DHMs using a cross-validation technique. Additionally, we employed the Non-Parametric ScottKnott ESD (NPSK) test to assess the ranking of the best DHMs. The study’s findings revealed that the most efficient deep hybrid model was achieved by using the EfficientNetB0 model in combination with a logistic regression classifier, achieving an impressive accuracy score of 96.14%. Our approach has the potential to significantly assist olive farmers in rapidly and accurately identifying diseases, thereby potentially reducing economic losses.