An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms

Author:

Igried Bashar1,AlZu’bi Shadi2ORCID,Aqel Darah2,Mughaid Ala1ORCID,Ghaith Iyad2,Abualigah Laith345ORCID

Affiliation:

1. Department of Information Technology, Faculty of Prince Al-Hussien Bin Abdullah II for IT, The Hashemite University, Zarqa 13133, Jordan

2. Faculty of Science and IT, Al-Zaytoonah University of Jordan, Amman 11733, Jordan

3. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan

4. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan

5. MEU Research Unit, Middle East University, Amman 11831, Jordan

Abstract

Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

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