Bacterial Disease Detection for Pepper Plant by Utilizing Deep Features Acquired from DarkNet-19 CNN Model

Abstract

In recent years, computer-aided agriculture applications have been developing rapidly as a prominent research area. In parallel with the developments in technology, the use of automatic systems, sensor fusion, the internet of things, and artificial intelligence-based systems is becoming widespread in agriculture. The use of these systems allows for safer, faster, and more cost-effective operations based on human factors in agricultural applications. Among these applications, there are artificial intelligence applications developed based on image processing and machine learning. Plant disease detection systems are also among these artificial intelligence studies. Within the scope of this study: I. It has been ensured that the leaf images of the pepper plant have been segmented and their features have been extracted from the pre-trained convolutional neural network. II. These obtained features have been classified through the classifier methods in order to detect bacterial disease. In the study, a total of 2475 images of pepper leaves with 1478 healthy and 997 bacterial diseases, which are among the PlantVillage data sets, have been used. To extract the features, the DarkNet-19 network model has been used as a pre-trained convolutional network. The SoftMax classifier in the last layer of the convolutional network model has been removed from the network and SVM, KNN, and Decision-Tree-based classifiers are used instead of it. According to the results, the level of performance achieved using the DarkNet-19 network and SVM classifier is quite satisfactory.

Keywords

• Deep Features
• CNN
• DarkNet-19
• Plant Disease

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