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Title	A Smart Surveillance Security For Crop Disease Detection Using Adaptive Approach Runge Kutta (Aark) Segmentation With Ai And Iot
ID_Doc	4791
Authors	Alagarsamy S.B.; Lonare L.T.; Bhatkar A.P.; Prabha Devi S.; Sivarajan S.; Sivaraju S.S.
Year	2025
Published	Information Security Journal
DOI	http://dx.doi.org/10.1080/19393555.2025.2506592
Abstract	Intelligent applications and services, including AI, IoT, robotics, next-gen wireless, and aerial networks, depend on advanced collaboration and communication technologies. This improves quality, energy efficiency, and connectivity in smart city applications, including public services, transportation, healthcare, monitoring, and surveillance. IoT systems can collect vast data, which deep learning analyzes for tasks like object detection. However, smart remote monitoring remains complex yet crucial for monitoring and control. This study proposes an IoT-based segmentation smart surveillance solution for multiple crop detection. It integrates deep learning, IoT, and cooperative drones to enhance smart city surveillance applications. We present an AI system for multi-crop segmentation using the deep learning-based Adaptive Approach Runge Kutta model (AARK). To enhance performance, employ deep transfer learning, data augmentation, and aerial drone data collection. We evaluate the segmentation paradigm’s performance using various ANFIS-based designs. The Adaptive Fuzzy rules enhance performance in complex environments. It leverages learning algorithms for accuracy and adaptability, making it ideal for control systems, fault detection, and dynamic simulations. Experimental results show that data augmentation improves multiple crop segmentation accuracy, enhancing overall system performance. The created system achieves 92% accuracy with Visual Geometry Group (VGG-16), 93% accuracy with ResNet-50 and 95% accuracy with MobileNet. © 2025 Taylor & Francis Group, LLC.
Author Keywords	AARK; artificial intelligence; IoT; remote sensing and aerial computing; segmentation

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