2. Architecture of an IoT system
2.1. Perception Layer
- Power—When far from power infrastructures (inside a building or engine vehicle), IoT devices need to be self-powered, which often means that they should host few sensors and stay awake for a short time: Low-Power IoT devices  are designed to be in a sleeping state for most of the time, and recently, there has been a growing interest in batteryless devices .
- Connectivity—An aspect related to power features and the amount of data to be communicated (payload); more diffused IoT-boards integrate/support radio modules for most diffuse networks (see Table 1). A relevant interest exists in Low-Power Networks whose IoT-oriented standards involve several organizations . A point not to be forgotten when choosing the board to be used for a given solution is the purpose of observation and the need for bidirectional communication.
- Modules—A major aspect affecting power management is related to the need of hosting power electronics as actuators (e.g., valves) or cameras. IoT devices comprise a main board and several shield/interfaces (GPS, SDcard, I2C, CAN) to connect external sensors.
- Physicality—A non-negligible aspect of a device relating to its external design , which includes that of the envelope/box; in the open air, devices are directly exposed to rain, freezing and high temperatures, hard winds, and other possible dangers which destroy electronic circuits and mechanical parts.
2.2. Network Layer
- Infrastructure protocols The radio networks mentioned above, besides an electric interface, also have a logic interface corresponding to the code of signal transmitted/received. Their standards, namely, IEEE 802.15.4 (WiFi), BLE (Bluetooth Low Energy), LTE-A, Z-Wave, etc. , include details about frequency range and modulation, the coding of data (packets, frames, datagrams), and features affecting the velocity of a network (see Table 2).The greatest difference in such sense is the connection procedure: in a traditional mobile network (such as GSM—Global System for Mobile communication) the device could take a (relatively) long time to access a radio network, and usually send long data packets (expensive handshakes, headers, etc.). LoRa, and especially Sigfox, on the other hand, allow Low-Power devices to wake-up, send a message, and sleep again in less than 1 s.
- Application protocols—They allow the exchange of chunks of data [35,36]. The most known of them is the Hypertext Transfer Protocol (HTTP), a foundation of communication for the World Wide Web. Though not specific for IoT applications, it is still used for traditional approaches. On the other hand, one of the most popular IoT protocols is represented by Message Queuing Telemetry Transport (MQTT). The Constrained Application Protocol (CoAP) is a web-based protocol that is used in constrained nodes and constrained networks. Extensible Messaging and Presence Protocol (XMPP) is based on exchanges of XML (eXtensible Markup Language) messages in real-time that are defined to connect devices to servers. Advanced Message Queuing Protocol (AMQP) is a queuing system designed to connect servers. Data Distribution Service (DDS) is a fast data bus for integrating devices and systems optimized for direct device communication (noncentralized). The main differences between them can be identified on the basis of publish and subscribe mechanisms, request/response interaction, security level, supported quality of services (QoS) mechanisms, and payload size . Other characteristics of network transport protocols are reported in Table 3.Other popular protocols oriented to low-power devices as LoRa (Long Range) and Sigfox, are optimized for specific connection requirements (e.g., uni-directional) and topologies and (e.g., decentralized).
- Service discovery—This class of protocols is used to detect devices and services offered through a network, reducing the effort to manage dynamic IoT systems without the need for human intervention. A well-known discovery architecture is the Domain Name System (DNS, which maps an IP address to a human-friendly name), which is extended by multicast DNS (mDNS) and DNS Service Directory (DNS-SD) to discover services by type and properties  in zero-configuration networks. In mDNS, resolution information is stored locally on each device, and each device directly answers incoming name resolution queries (each device acts both as a server and a client). DNS-SD defines how a client queries DNS servers to discover services within a domain using the service type as a selection criterion; a client gathers the descriptions of all services and selects the most appropriate. This reduces the scalability of the protocol in large networks .
2.3. Service Layer
- Device management—In IoT, device management plays a fundamental role: IoT systems could consist of fleets (from hundreds to millions of devices) of devices, to be securely accessed, and kept up-to-date. In FIWARE , each device type is interfaced by a specific agent, whose goal is to translate IoT-specific protocols into data exchange or information necessary to control the device. Device discoverability, together with most of the meta-data information on sensors, are increasingly maintained by ontology-based systems .
- Data ingestion—This refers to the gathering of raw data from things (devices) to a repository. This process can be either performed periodically, by pulling data from sources into the repository, or continuously, by letting sources pushing data streams into the repository. Examples of data ingestion technologies are Kaf  and AWS .
- Data storage—Data are collected into databases, structured and persistent repositories organized atop a single conceptual model , classically represented by a relational database (e.g., W3School ). However, the volume, variety, and complexity of data demand for distributed and elastic storage systems increased the usage of OORDBs (Object Oriented Relational Data Bases, e.g., [55,56]), recently further generalized to data lakes (e.g., examples of data lake implementations are Ama  and Azu ), that is, central repositories where raw data are organized in zones depending on the elaboration to which they are devoted. Data lakes store raw data, as is, into their original format, therefore, they eliminate the up-front costs of transforming data into a format suitable for a database, opening data access to every thing/user in the IoT ecosystem .
- Data processing—Such a functional process is aimed at extracting meaningful information from raw data. Processing may start during data ingestion, for instance when extract, transform, and load (ETL) procedures are applied before data storage (e.g., transforming raw data before copying them into a relational database). Depending on both the responsiveness and the data necessary to back the decision-making process, data processing takes different places. We distinguish processing at embedded, edge, and cloud computing levels (Figure 4). Indeed, processing can be carried out on a single board (embedded computing), on network devices (edge computing), and on remote data servers (cloud computing). Cloud computing allows highly scalable processing at the cost of moving data from IoT devices to data centers spread worldwide; processing can be based on data from the whole system at the cost of higher latency that is not negligible for real-time applications. “Edge” computing brings processing closer to the IoT devices by allowing data processing on internet access points (e.g., routers). This reduces the overall network latency and allows the processing of smaller data aggregates . “Embedded” computing moves processing to the “thing” itself, eliminating network latency at the cost of lower processing resources (to overcome these limits, technologies such as FPGA are developed). Well-known processing models are streaming, minibatch, and batch. Streaming allows the processing of single data items as soon as they are pushed into the data stream. Minibatch allows the processing of a window (e.g., a time window) of data items pushed into the stream. Batch processing supports the processing of large volumes of data items at once. While the latency of stream or minibatch processing is in the order of seconds or minutes, batch processing has latency measured in hours. Examples of frameworks supporting distributed processing at the cloud computing level are Spark  and MapReduce , while Google recently introduced the Global Mobile Edge Cloud  to enable edge computing on 5G networks. At the embedded level, processing can be implemented by directly programming the “things”.
3. IoT in Crop Management
- Weather—Stations are present even in many non-experimental farms. The classical outfit is that of a climatic station: rain-gauge, temperature, and relative humidity. In the 1970s, pan evaporimeter was also added in agro-weather stations, while radiation, wind velocity/direction, and leaf wetness became more frequent from the 1980s with the diffusion of electronic stations. Less frequent is the availability of soil temperature.
- Water availability—Water is recognized as the most important production factor (e.g., GRIDA ); in dry-summer regions (Mediterranean areas) rainfall trends determine huge risks in growing a crop, as prolonged drought in conjunction with high temperatures in a sensitive period (e.g., seedling, flowering) have dramatic effects on yield. As water availability is not always an option, it has a main role in crop choice (e.g., irrigated vs. rain-fed) and checking soil availability in terms of water content (e.g., ) or soil water potential (e.g., ), or directly by direct observation of plant status (IR sensors); water excess scenarios are no less dangerous to a crop: rainfall of long duration or high intensity, as much as an unexpected hail can do no less damage to a crop (as they do to humans); drainage systems, relevant to hydrological network management, together with channel, storage, and distribution systems, become really important for water supply.
- Fertility—Nutritive substances are essential for plant growth, and in many cases fertilizer is applied along the growing season (e.g., foliage fertilizer); soil water sensors often include electric conductivity, used to deduct information on soil nutrient contents. More reliable information on the nutritional state of a crop can be obtained from multispectral and hyperspectral camera sensors set on field cameras.
- Pests and diseases—Detecting the presence and development stage of pests and disease, spreading of insects and weeds is fundamental in growing a single species. A main activity of every farmer is maintaining an artificial ecosystem and preventing its shift toward a community of species that deteriorate the quality and quantity of expected yield. Specific sensors are available to the purpose and are already used in agro-weather networks as leaf wetness.
- Other production-related aspects—Detectors for carbon dioxide and other gases (IRGA) are used (mostly for research purposes) to monitor plant and soil respiration rates, including GHG emissions; IR sensors are also used for detecting heat anomalies (we already mentioned water stress) as the presence of flames and intrusions (PIR) from hot blood animals, eventually integrated with cameras. Increasing is the interest in canopy monitoring by multipurpose cameras with sensors of variable sensitivity.
- Transponders—Machines have a particular role in control. They are a part of technology and a production factor; they need to be controlled to be in a good working state, and under constant survey in the case of autonomous vehicles because of dangers and damages that failures may represent for human beings, crops, and the environment. Moreover, vehicles may host sensors for self-monitoring , and fields from varying distances (UAVs), allowing the increase of spatial detail and time resolution of most sensing tasks listed above.
3.1. Using Observations
3.2. Existing Applications
- Monitoring environment (air, soil, water), crops (plant), and animals—62%;
- Remote control in irrigation, fertilization, pesticides, lighting, intrusions—25% of papers;
- Prediction of environmental conditions, production, growth—6% of papers;
- Logistics—7% of papers.
3.3. Case Study—Irrigation Scheduling
- Direct estimate of “crop stress”, based on remote/proximal canopy sensing. Satellite sensing , recently integrated with those of drone images . Proximal measurement of canopy temperature by IR sensors (e.g., Jones et al. ) and field IR cameras integrated with an IoT system are also adopted  to the purpose.
- Water availability in soil, based on direct “soil moisture” observation, then use the lower and upper thresholds criteria as in the previous method, to get advice on a possible water stress condition .
- Water availability by “water budget”, based on the estimate of water loss of a canopy (Evapotranspiration—ET ), from observed temperature, relative humidity, wind speed, and solar radiation. ET is used as a boundary condition to a soil water redistribution model to estimate soil water status. Logical (if-then) rules are finally used to produce irrigation advice .
- Direct observation of stress can be guessed from cameras that substitute direct farmer monitoring of surfaces: those from satellite, air-crafts, and UAVs (Visible or IR) can also help (by indices as NDVI) by identifying anomalous conditions in the cropped surface, which is supposed to be homogeneous, and made available to an assistance service. However, imagery needs interpretation and lacks subsurface information.
- Soil water content can give more information on the water status of a rooted zone. Nonetheless, identification of threshold values for water supply is still based on empirical knowledge based on soil and plant type, therefore with a local validity. Moreover, sampling a surface requires a number of sensors with prohibitive maintenance costs.
- For water budget potential, ET has to be complemented by empirical correction coefficients to obtain the real crop water requirement. Moreover, soil dynamics coefficients are required to estimate “Flow Intensity”, whose general physical low is well-known while parameters are subject to high spatial heterogeneity and temporal variations .
- Devices for field monitoring, of the soil–plant–atmosphere system. In the majority of cases, continuous monitoring is not required, therefore, the “perception layer” is conceived as a network of low-power devices that sleep for most of the time, supported by a radio network with an easy connection protocol. Additionally, they would mainly be for metering purposes and bi-directional communication, though facilitating reconfiguration, could prove unnecessary. The payload is expected to be reduced and messages are allowed to have a high-latency, though with a high QoS (quality of service). Low-power networks such as Sigfox and LoRa could be a good choice, though most recent networks (WiFi-halow, NB-IoT, CAT-M1) reduce constraints and allows both usage of protocols to be managed (MQTT) and messages to be digested (FIWARE) more easily. Such solutions can be adopted by almost every board of class “Arduino” that, together with deep-sleep mode, includes easily configurable electrical interfaces (e.g., I2C), together with a wide availability of shields (e.g., Real-Time Clock and SD card).
- Power devices, such as actuators and cameras require a different approach, and devices with embedded computing could be required, based on nano- or single-board computers (e.g., Raspberry Pi), already adopted in the “wired” agriculture (e.g., hydroponics). They allow for continuous monitoring (and surveillance) of plants, actuators, and intrusions (including animals) and need low-latency/real-time response/alerts to be sent to a supervisor (farmer). In these cases, a reliable wireless connection is required, which, if properly optimized, can profit from networking technologies mentioned in the previous point. Nanocomputers include LAN connectors and common wireless connection interfaces and are robust enough to be set in the outer environment, but need to be adequately power supplied (Photovoltaic systems and high-duration batteries). Their use in UV/AV enhances the spectrum of application of IoT for decision support  allowing the collection of vehicle data, failure events, and actions performed by tools.
- Storage—Most of the cases reported in the literature are pilot projects that use a limited number of devices, showing reduced exploitation of cloud computing potential. Major needs seem to be represented by data security, service outsourcing, and No-SQL data storage due to information heterogeneity (inventories, satellite images, mobile platform mission data, etc.). However, the number of IoT devices/solutions is growing and, though at present such storage systems are mostly for research purposes and country-level surveys, the increase of detail of information in space and time would soon require data-lake storage and big data.
- Processing—As already put in evidence, though UV-oriented protocols (e.g., AVI-Link and ROSlink protocol) are currently used also in UAV guidance. Most IoT applications do not require real-time performances (latency < 1 s). Data collected from mobile platforms and field monitoring stations are mostly batch-processed and delivered to end-users by APP dashboards. Decision-oriented information and supervised actuation are also provided (switching irrigation valves, heaters, vents, etc.) by the processing framework. Direct commands operated by an artificial intelligence system are still bounded to industrialized cropping systems (hydroponics and greenhouses).
- IoT is an enabling and mature technology, proven to be able to accelerate the adoption of SF.
- Relying on IoT, many solutions to Smart Farming and Farm Management Systems are going to be accessible even to small farm holders.
- IoT allows increasing access to crop monitoring and significantly enhances the availability of information and early warnings which, in turn, provide more reliable predictions and decision-making support to farmers, managers, and policymakers.
- Excitement in IoT is pumping the belief that a large number of cheap sensors could increase data granularity in space and time with an acceptable decrease in data quality. However, data (sensor) reliability remains a fundamental aspect of any technology.
- ML is, to date, too focused on solving problems, underestimating the data requirement for learning stage and the need for explainable knowledge oriented to enhance models for simulation of bio-agro-ecological, soil-plant-atmosphere, and value-chain systems.
- Ethical aspects also emerge. Industrialization and spreading of micro-IoT-devices, envisaging fleets of “artificial insects”, could require strong regulations,  including a protocol for placement, location, and recollection.
Conflicts of Interest
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|LoRaWAN||2–5 km||country dep.|
|Sigfox||3–50 km||900 MHz|
|Neul||10 m||country dep.|
|Technology||Data Rate||Technology||Data Rate|
|Bluetooth||1 Mbps||GMS||35–170 kbps|
|Wifi||600 Mbps||EDGE||120–384 kbps|
|ZigBee||250 kbps||UMTS||384 Kbps–2 Mbps|
|LoRaWAN||–50 kbps||HSPA||600 kbps–10 Mbps|
|Sigfox||10–1000 bps||LTE||3–10 Mbps|
|Neul||1–100 kbps||LTE-M1||10 kbps|
|FMIS/PA/machine activity/resource usage||[85,86]|
|Crop monitoring||environmental sensing||[3,36,63,81,89]|
|detect crop stress/diseases/pests/weeds/ripening||[82,91,96,97]|
|Crop practices||smart farming/remote control/automation||[2,32,36,84,98,99,100]|
|precision practices/prescription maps||[5,36,85]|
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