AI-IoT based smart agriculture pivot for plant diseases detection and treatment

被引：0

作者：

Amin S. Ibrahim ^{[1
]}

Saeed Mohsen ^{[2
]}

I. M. Selim ^{[3
]}

Roobaea Alroobaea ^{[4
]}

Majed Alsafyani ^{[5
]}

Abdullah M. Baqasah ^{[5
]}

Mohamed Eassa ^{[6
]}

机构：

[1] Ahram Canidian University (ACU),Electronics and Communication Department, Faculty of Engineering

[2] Al-Madinah Higher Institute for Engineering and Technology,Department of Electronics and Communications Engineering

[3] King Salman International University (KSIU),Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering

[4] Sadat City University,Faculty of Computer and Artificial Intelligence

[5] Taif University,Department of Computer Science, College of Computers and Information Technology

[6] Taif University,Department of Information Technology, College of Computers and Information Technology

[7] October 6 University,Department of Computer Science, Faculty of Information Systems and Computer Science

[8] Applied Science Private University,Applied Science Research Center

来源：

Scientific Reports | / 15卷 / 1期

关键词：

Artificial intelligence (AI); Internet of Things (IoT); Smart agriculture; Unmanned aerial vehicle (UAV); Pivot; Plant diseases detection; Deep learning (DL);

D O I：

10.1038/s41598-025-98454-6

中图分类号：

学科分类号：

摘要：

There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases and transforming the agriculture industry into robustness and ecologically conscious. Various IoT smart agriculture techniques are escalated in this field to solve these challenges such as drop irrigation, plant diseases detection, and pest detection. Several agriculture devices were installed to perform these techniques on the agriculture field such as drones and robotics but in expense of their limitations. This paper proposes an AI-IoT smart agriculture pivot as a good candidate for the plant diseases detection and treatment without the limitations of both drones and robotics. Thus, it presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops and controlling their actuators for the plant diseases treatment. For the plant diseases detection, the paper augments a dataset of 25,940 images to classify 11-classes of plant leaves using a pre-trained ResNet50 model, which scores the testing accuracy of 99.8%, compared to other traditional works. Experimentally, the F1-score, Recall, and Precision, for ResNet50 model were 99.91%, 99.92%, and 100%, respectively.

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