Source of Publication
Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases.
Institute of Electrical and Electronics Engineers (IEEE)
Diseases, Deep learning, Feature extraction, Agriculture, Support vector machines, Neural networks, Image color analysis
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This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Khattak, Asad; Asghar, Muhammad Usama; Batool, Ulfat; Asghar, Muhammad Zubair; Ullah, Hayat; Al-Rakhami, Mabrook; and Gumaei, Abdu, "Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model" (2021). All Works. 4328.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series