The industry of agriculture provides the world with biological products that are sensitive to both environmental conditions variation and applied management practices [1
]. Therefore, it is critically necessary that farmers access the information where these variations exist in their fields, in real-time, so they can adjust their practices accordingly and promptly. The need for real-time access to climatic data helps in monitoring and facing the escalation in the frequency and intensity of potentially dangerous events such as drought, heavy rainfall, flooding and extreme temperatures. As climate change is already approved to hamper agricultural growth, it is estimated to have already reduced global yields of maize and wheat by 3.8% and 5.5% respectively [3
]. Thus, the major challenge identified to be addressed is to provide farmers, through precision/smart agriculture, with the required information about the climatic conditions, in a timely fashion, instead of the traditional site-specific management practiced before. For this reason, agriculture could not be left out of the technological advances taking place worldwide in all scientific fields of research. Precision farming encompasses a large array of different technologies incorporating sensors, information systems, enhanced machinery, and decision supporting systems with a main goal of re-organizing the whole agricultural system in the direction of a low-input and high-efficiency for a sustainable agriculture [4
]. To attain such a goal, precision farming provides the means for observing, assessing and controlling agricultural practices from the quotidian herd management through horticulture to field crop production. The industry of agriculture is, at present, capable of collecting more inclusive data on different production controlling parameters in both space and time [6
]. It provides a decision support system for delivering a large vision about the possible treatments, whether field-wide or for only specific parts of the field, and the means for taking the proper reaction according to the data collected [7
]. Nowadays, the quick evolvement of technology in the fields of IoT (Internet of Things), UAS (Unmanned aerial systems), low power devices and sensors, is opening up new frontiers in the agricultural applications. In this regard, it is possible to convert the traditional farm approach into the “Smart Farm” philosophy, which allows the access for more accurate monitoring of crop development and health status with adequate temporal, spatial, and spectral resolutions [8
]. Furthermore, a UAS platform with proper sensors, based on wireless sensor network (WSN), is becoming a common combination in this field of research offering a flexible, convenient, and cost-effective way to provide enquiries about the desired observations on agricultural parameters [9
]. In this regard, Di Francesco et al. [10
] have provided a full literature survey about the use of WSN along with mobile vehicles generally, including robots, terrestrial vehicles, and also UAS. Moreover, Zhan et al. [11
] have moved a step further and proposed an optimization solution to jointly ameliorate the WSN wake-up schedule with the drone’s pathway in order to minimize the energy consumption.
UAS are aerial vehicles that can be presented differently depending on their shapes and sizes, and can be remotely controlled or can fly autonomously throughout a software-controlled flight on the basis of a GPS system [12
]. The use of a light composite materials in the made-up of UAS helps to reduce their weight and increase their position-changing capability with a strong potential to fly in high altitudes depending on data collection needs. Due to the various navigation systems and recording devices that can be possibly embedded in the UAS, they can travel inaccessible areas providing the real-time monitoring, through dedicated payloads, of the state of health of the crops and therefore quick raw data of different agro-metrological parameters [13
]. Sensor networks, in conjunction with UASs, are often implemented in research settings to expedite the process of data collection and to increase the breadth of data that may be collected over a geographical area. This conjunction is applied normally to monitor marine-coastal environments for environmental metrics data collection, such as water temperature, salinity and pH. Moreover, it is potentially useful to monitor agrometeorological data in remote areas (i.e., vineyard and olive cultivation in steep slopes, forests on cliff, large trees in urban environment, etc.), and also to monitor greenhouse atmospheric gas concentrations and many other examined applications in mapping, feature detection, and monitoring wildlife.
IoT is defined as the worldwide network of interconnected objects (devices, mechanical and digital machines, animals or people) uniquely addressable based on standard communication protocols, that are provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction [16
]. In the agricultural field, IoT allows us to increase the monitoring points of agro-meteorological parameters and to remotely control the agro-actuators, with reduced costs compared to the traditional approach. It owes much of its success to the widespread distribution of Internet communication networks (without this, it would not be applicable), even in agricultural areas.
In Italy, there are still large portions of territory, mostly in the Apennines mountain chain, not served by communication networks due to low population density. These areas host agricultural and forestry activities located at different altitudes, which is difficult to monitor. The use of WSN for monitoring climatic changes such as temperature degree and relative humidity in agricultural fields has been studied considerably but substantial applications are still rare [18
]. Therefore, new solutions are needed in order to monitor constantly and remotely the data of interest, overcoming the barriers given by lacking communication networks and difficulties to reach hostile territories. For this reason, this work focuses on the properties of a new sensor called “AgriLogger”, coupled with an UAS system, capable to collect, store for long periods and transmit agro-meteorological data, presented by temperature and relative humidity, in remote areas that are hard to reach and not served by telecommunication networks.