This paper discusses the design and performance of a frequency reconfigurable antenna for Internet of Things (IoT) applications. The antenna is designed to operate on multiple frequency bands and be reconfigurable to adjust to different communication standards and environmental conditions. The antenna design consists of monopole with one PIN diode and 50Ωfeed line. By changing the states of the diode, the antenna can be reconfigured to operate in a dual-band mode and a wideband mode. The performance of the antenna was evaluated through simulation. The antenna demonstrated good impedance matching, acceptable gain, and stable radiation patterns across the different frequency bands. The antenna has compact dimensions of (26×19×1.6) mm3. It covers the frequency range 2.95 GHz -8.2 GHz, while the coverage of the dual- band mode is (2.7-3.8) GHz and (4.57-7.4) GHz. The peak gain is 1.57 dBi for the wideband mode with omnidirectional radiation pattern. On the other hand, the peak gain of the dual-band mode is 0.87 dBi at 3 GHz and 0.47 dBi at 6 GHz with an omnidirectional radiation pattern too.
In smart cities, health care, industrial production, and many other fields, the Internet of Things (IoT) have had significant success. Protected agriculture has numerous IoT applications, a highly effective style of modern agriculture development that uses artificial ways to manipulate climatic parameters such as temperature to create ideal circumstances for the growth of animals and plants. Convolutional Neural Networks (CNNs) is a deep learning approach that has made significant progress in image processing. From 2016 to the present, various applications for the automatic diagnosis of agricultural diseases, identifying plant pests, predicting the number of crops, etc., have been developed. This paper involves a presentation of the Internet of Things system in agriculture and its deep learning applications. It summarizes the most essential sensors used and methods of communication between them, in addition to the most important deep learning algorithms devoted to intelligent agriculture.