Ayid Yasser M, Fouad Yasser, Kaddes Mourad, El-Hoseny Heba M
Mathematics Department, Applied Collage Al-Kamil Branch, University of Jeddah, Jeddah, Saudi Arabia.
Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt.
PLoS One. 2025 May 23;20(5):e0324347. doi: 10.1371/journal.pone.0324347. eCollection 2025.
The agricultural sector faces critical challenges, including significant crop losses due to undetected plant diseases, inefficient monitoring systems, and delays in disease management, all of which threaten food security worldwide. Traditional approaches to disease detection are often labor-intensive, time-consuming, and prone to errors, making early intervention difficult. This paper proposes an intelligent framework for automated crop health monitoring and early disease detection to overcome these limitations. The system leverages deep learning, cloud computing, embedded devices, and the Internet of Things (IoT) to provide real-time insights into plant health over large agricultural areas. The primary goal is to enhance early detection accuracy and recommend effective disease management strategies, including crop rotation and targeted treatment. Additionally, environmental parameters such as temperature, humidity, and water levels are continuously monitored to aid in informed decision-making. The proposed framework incorporates Convolutional Neural Network (CNN), MobileNet-1, MobileNet-2, Residual Network (ResNet-50), and ResNet-50 with InceptionV3 to ensure precise disease identification and improved agricultural productivity.
农业部门面临着严峻挑战，包括因未被发现的植物病害导致的大量作物损失、低效的监测系统以及病害管理的延迟，所有这些都威胁着全球粮食安全。传统的病害检测方法通常劳动强度大、耗时且容易出错，使得早期干预变得困难。本文提出了一个用于自动作物健康监测和早期病害检测的智能框架，以克服这些局限性。该系统利用深度学习、云计算、嵌入式设备和物联网（IoT），在大面积农业区域提供有关植物健康的实时洞察。主要目标是提高早期检测的准确性，并推荐有效的病害管理策略，包括作物轮作和靶向治疗。此外，还持续监测温度、湿度和水位等环境参数，以辅助进行明智的决策。所提出的框架结合了卷积神经网络（CNN）、MobileNet-1、MobileNet-2、残差网络（ResNet-50）以及带有InceptionV3的ResNet-50，以确保精确的病害识别并提高农业生产率。
