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Internet of Things is one of the most popular subjects nowadays where sensors and smart devices facilitate the provision of information and communication. In IoT, one of the main concepts is wireless sensor networks in which data is collected from all the sensors in a network characterized by low power consumption and a wide range of communication. In this study, an architecture to monitor soil moisture, temperature and humidity on small farms is provided. The main motivation for this study is to decrease water consumption whilst increasing productivity on small agricultural farms and precisions on them. This motivation is further propelled by the fact that agriculture is the backbone of some towns and most villages in most of the countries. Furthermore, some countries depend on farming as the main source of income. Putting the above-mentioned factors into consideration, the farm is divided into regions; the proposed system monitors soil moisture, humidity and temperature in the respective regions using wireless sensor networks, internet of things and sends a report to the end user. The report contains, as part of the information, a 10-day weather forecast. We believe that with the above information, the end user (farmer) can more efficiently schedule farm cultivation, harvesting, irrigation, and fertilization.

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The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.

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