Today, despite the idea that people may have about agriculture, it is a complex, time-consuming, and expensive process. Still, the reality is that today's agriculture industry is data-driven, accurate, smarter, and even easier. It has changed more compared to the past. All these cases have been formed with the help of a new concept called the Internet of Things in the agricultural industry. The Internet of Things is a huge network of people and things and the increasing expansion of the Internet and the reduction of its costs have provided the ground for the creation of the Internet of Things. Such changes have caused a great revolution in the field of agricultural industry, which has shaken the existing farming methods and can create new opportunities in the present and future. Determining the users of the Internet of Things will play an effective role in determining its prospects. However, the implementation of the Internet of Things is associated with challenges, and the Internet of Things needs standards to continue its work. So far, a lot of research has been done on the challenges of the Internet of Things and ways to solve them. Also, standards have been defined for the Internet of Things. In this article, we intend to examine the concept and applications of the Internet of Things in agriculture and irrigation, then the challenges and ways to solve them, and the architectures and standards proposed in the field of the Internet of Things. Also, this paper highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges that are expected to be faced when integrating this technology with traditional agricultural practices. On the other hand, IoT devices and communication techniques related to wireless sensors encountered in agricultural applications and sensors available for specific agricultural applications, such as soil preparation, crop status, irrigation, insect and pest detection, as well as How to use this technology by the producers, which will help them to carry out the stages of cultivation, from planting to harvesting, packaging, and transportation, has been explained. Advanced IoT-based architectures and platforms used in smart irrigation are also highlighted wherever appropriate. Finally, based on this comprehensive review, we identify the current and future Internet of Things trends in smart irrigation and highlight potential research challenges.

For this purpose, a moisture sensor device was designed and constructed in February and March 2019 to determine the appropriate time to stop irrigation in furrow irrigation. Testing the device in the laboratory and its application in the Farm of the Campus of Agriculture and Natural Resources, University of Tehran (Mohammad Shahr), Iran, from April to July 2019. The purpose of this study was to evaluate the performance of a smart sensor of soil moisture to determine the optimum depth of installation and recording of soil moisture at 10, 30, and 50 cm depths and different length ratios in furrow irrigation. Initially, calibration of the device was carried out on field soil, and based on the obtained validation, the device was transferred to the field. To achieve the goals of optimum depth of installation and optimum length, 36-meter furrows with a distance of 0.75 m were created in the field. Sensitive lengths in furrows with 0.5 L, 0.75 L, and 0.85 L ratios were selected as the starting points. The results showed that in the calibration and validation phases, the R2 values were 0.93 and 0.95, respectively, and in the calibration and validation stages, the value of nRMSE was 80 and 13.81%, indicating good model training in the calibration stage. Also, the average RE parameter in estimating soil moisture was 2.74%, indicating the high accuracy of the device in estimating soil moisture. The results also showed that if the device was installed at a depth of 30 cm from the soil surface of the furrow and at 75% from the beginning of the field, the depth and runoff losses would be minimal and irrigation adequacy would be best compared to other depths and lengths. It is expected that with optimal water consumption and timely interruption of irrigation, deep losses and runoff will be avoided, and with low water consumption, the productivity of crops will increase.

The assessment of sprinkler system performance is crucial in ensuring the efficient use of water resources. The commonly used indicators of the uniformity of water distribution in sprinkler systems are Christiansen's uniformity (CU) and distribution uniformity (DU) coefficients. A more comprehensive analysis of water distribution is essential in situations where the reliability of these coefficients as indicators of water distribution patterns is limited. In the present research, distribution maps of water depth were prepared from water application profiles using catch cans experiments that were carried out in the research farm of the Agriculture and Natural Resources Faculty of the University of Tehran, which is located in Alborz province. In this way, water application profile data were obtained at different operating pressures (200, 250, 300, 350, and 400 kPa). By using this data, 2D and 3D distribution maps of water depth were created due to the overlapping of sprinklers in different arrangements, spacing, and pressures. In addition, CU and DU coefficients in square, rectangular, and triangular arrangements with different spacing and pressures from 200 to 400 kPa. A total of 11,250 different simulations were calculated and analyzed. Distribution maps of water depth contribute to advancing the understanding of sprinkler irrigation system performance and aid in the optimization of water management practices. Key words: agriculture, irrigation planning and efficiency, operating pressure, water management, water supply HIGHLIGHTS • CU and DU coefficients provide only an overall measure of uniformity and do not demonstrate the spatial variability of water depth, which could lead to over-or under-irrigation in certain areas of the field. • Water depth distribution maps offer a more accurate assessment of spatial variability and allow for the identification of specific areas that require attention to improve sprinkler system performance.