Agricultural Monitoring And Management Based On Internet Of Things, Data Analytics And Artificial Intelligent Technologies: Review

摘要

The increase in productivity of agricultural processes is essential to improve yields and costeffectiveness using new technology like Internet of Things(IoT) and Artificial Intelligent (AI). The improvement of agricultural sensor, machine learning, wireless communication, Big data and Cloud computing technologies, stimulates the IoT in agriculture. IoT is playing a vital role in innumerable areas of protected agriculture. This study aim to analyze the recently developed IoT technologies applicable in agricultural industries. Data for this study were mined from 40 recently peerreviewed scientific publications (20152019) targeting on the frequently of use of the mentioned technologies in agricultural industry. Results from the reported studies reveals that Bluetooth and LoRaWAN technologies were the most useful technologies among the reviewed technologies with (15%) followed by Mobile cellular and ZigBee technologies at about (13%) of application. However, NBIoT technology comes the most applicable technology after ZigBee technology with about (12%). From the data collection, SigFox seems to be the next most applicable technology among the reviewed studies in agricultural industry after NBIoT with about (11%). WiFi were found the most useful technology after SigFox with an application rate of about (8%). The least applicable rate of technologies from the reviewed studies were found to be 6LowPAN and NFC with similar application rate of about (7%). The study also reveals that Machine Learning technologies were the most useful computational technologies in farm monitoring among the reviewed technologies with the application rate of (32%) followed by Edge Computing with the application rate of 24%. Cloud computing technology was happened with an application rate of 23% followed by Big data computing technology with the application rate of 21%. However, results also reveals that Big data analytics were the most useful computational technologies in animal health monitoring among the reviewed technologies with the application rate of (29%) followed by Cloud computing with the application rate of 27% and Edge computing with an application rate of 26%. Machine Learning technology were found with the application rate of 18% as expressed in section VI of the study.