A5088540080- Smart Agriculture and AI
- Fire effects on ecosystems
- Fire Detection and Safety Systems
- Video Surveillance and Tracking Methods
- Remote Sensing and LiDAR Applications
- Structural Analysis and Optimization
- Date Palm Research Studies
- IoT Networks and Protocols
- Energy Harvesting in Wireless Networks
- IoT and Edge/Fog Computing
- Dental Radiography and Imaging
- Modular Robots and Swarm Intelligence
- Advanced Chemical Sensor Technologies
- Anomaly Detection Techniques and Applications
- Machine Fault Diagnosis Techniques
- Advanced X-ray and CT Imaging
- Advanced Materials and Mechanics
- Fault Detection and Control Systems
Adana Science and Technology University
2023-2024
Cukurova University
2021-2022
The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization control, early-stage pest crop disease management, energy conservation. application protocols such as ZigBee, WiFi, SigFox, LoRaWAN are commonly employed...
Forest ecosystems face increasing wildfire threats, demanding prompt and precise detection methods to ensure efficient fire control. However, real-time forest data accessibility timeliness require improvement. Our study addresses the challenge through introduction of Unmanned Aerial Vehicles (UAVs) based database (UAVs-FFDB), characterized by a dual composition. Firstly, it encompasses collection 1653 high-resolution RGB raw images meticulously captured utilizing standard S500 quadcopter...
Non-chemical weeds control is a crucial aspect of sustainable organic agriculture. This paper explored the state-of-the-art deep learning networks applicability for weed detection and proposed novel, simple model. The network, CovWNET, second smallest network among implementations compared in this study. It has roughly 1/3 times more parameters; however, it achieves 1.8% higher accuracy than MobileNetV2. Correspondingly, CovWNET approximately five fewer parameters 2 % less best achieving...