Ground or proximal sensors play a key role in Precision Agriculture (PA). Proximal sensing is defined as the use of field sensors to obtain signals from the analyzed feature (e.g., climate, soil, or plant) when the sensor is in contact with or close to it (within a few meters) ([17,18]). Nowadays, there are many types of proximal sensors capable of monitoring multiple parameters related mainly to the plant water, nutritional, and health status [19]. Another classification includes the division into static proximal sensors, which remain stationary in the field, or mobile proximal sensors, mounted on vehicles or robots [20].

It is well known that climate is one of the main aspects determining plant growth and outputs [21]. This occurs because each plant is sensitive to certain growing conditions such as air temperature, relative humidity, wind, soil temperature, and light. Thus, it is crucial for farmers to understand the climatic conditions on their farms [22]. Hence, meteorological information may help the farmer make the most efficient use of natural resources to improve agricultural production. Among the most common proximal sensors are those that can assess climatic parameters. Usually, such sensors are mounted in small weather stations in the field, and a basic weather station usually consists of a temperature and humidity sensor, a wind speed sensor, a sensor that measures precipitation height, and one that can assess solar radiation information.

Another category of ground-based sensors is soil-based sensors. New-generation proximal soil-based sensors are able to monitor real-time physical and chemical soil parameters, such as moisture, temperature, pH, soil nutrients, and pollutants, providing key information to optimize crop cycle management, combat biotic and abiotic stresses, and improve crop yields.

One of the most common uses of such sensing is for irrigation management, through the capability of estimating the crop reference evapotranspiration (ETO), returning to the plant the full or partial (depending on the farm irrigation strategy) amount of water lost by evapotranspiration, using the FAO-56 Penman–Monteith equation recommended by the United Nations (UN) Food and Agriculture Organization (FAO) [23]. Weather observations and forecasts, coupled with physical observations, can help predict the development of the main pests and can be used to schedule control actions to prevent pest development. Thanks to such information, it is possible to change plant microclimate and influence the habitability for pests, for example, through pruning operations to reduce internal canopy humidity and reduce the probability of infection by plant pathogens. Integrated pest management has been a response to reduce the environmental impact of chemical pesticides [24].

For irrigation scheduling nowadays, there is a tendency to focus on plant-based sensors. Common and reliable sensors capable of continuously estimating plant water status include leaf turgor sensors, devices capable of assessing leaf turgor pressure, a parameter directly related to plant water status; sapflow sensors, capable of providing indications of the plant transpiration flows; trunk dendrometers, capable of monitoring trunk fluctuations over time, dependent on the plant hydration status; and Linear Variable Differential Transformer (LVDT) fruit gauges [25]. The latter are low-cost devices that can continuously and very accurately monitor fruit development over the day, providing information about plant water and nutritional status during the fruit growth stage ([26,27]).

Although plant-based sensors for monitoring plant water status are among the most common, there are other devices that are very useful for crop management. Foliar wetness sensors are devices installed inside the canopy to assess its moisture status, preventing the rise of pathogens and diseases [28]. Optical sensors, working in the visible/near-infrared band, can be useful for estimating the nutritional and health status of the plant ([29,30]). On the other hand, Light Detection and Ranging (LiDAR) technology sensors can be used to assess and measure canopy shape and volume [31].

Last-generation sensors allow continuous data acquisition, greatly increasing the degree of information without increasing the farm workload. Moreover, these sensors can be used to create IoT networks for various applications [32]. IoT focuses primarily on providing many small, interconnected devices, mainly using WSN technology, that can work together with a common purpose [33]. A WSN has as a main target of offering sensing and monitoring capabilities utilizing wireless technologies to connect to sensing devices. In WSNs, data collection and transfer occur in four stages: collecting the data, processing the data, packaging the data, and transferring the data [34]. With the help of WSN technologies, farmers can analyze weather conditions, water use, energy use, soil conditions, and plant morpho-physiological parameters collected from their farm feeding decision-support systems (DSSs) [35]. WSN has to fulfill requirements such as long range, low energy, and adequate data rate. Several technologies, including Long-Range Wide Area Network (LoRaWAN) [36], Narrow Band IoT (NB-IoT) [37], SigFox [38], and Long-Term Evolution for Machines (LTE-M) [39], are arising as candidates in the upcoming transition to 5G communications [40].

This discussion uses the term IoT to collectively describe the different technologies associated with ground-based proximal sensing.

UAVs were used in agriculture in 1997 for the first time. This technology was first used in Japan and South Korea, where mountainous terrain and relatively small family-owned farms required lower-cost and higher-precision spraying. Historically, the use of aerial application of pesticides was prohibited by the European Union, inhibiting the growth of UAV application in agriculture. Later use of UAVs was centered mainly around aerial imagery.

Nowadays, UAVs are applied in PA to collect images of high quality, mounting adequate sensors to this end. Sensor choice is carried out carefully according to a number of parameters such as resolution, optical quality, weight, captured images, and price. UAVs may carry multiple types of sensors: RGB (red–green–blue), NIR (near-infrared), IR (infrared), multispectral (MS), and hyperspectral (HS) cameras. RGB and NIR bands are useful for collecting information about vegetation stress and chlorophyll content [41]. Furthermore, LiDAR sensors can also be used in environmental sciences for terrestrial scanning, obtaining information on crop height or canopy size [42].

Each type of sensor can be utilized for monitoring diverse parameters in vegetation. RGB is low in cost and useful for UAV applications of precision farming, such as the creation of orthomosaics, as they can capture images with high resolution. In addition, they are useful in different conditions (sunny and cloudy weather). But they cannot analyze many vegetation indices due to their limited spectral range. MS and HS sensors, compared to RGB, collect data in different spectral channels and acquire images with high quality that are useful for studying a multitude of physical and biological characteristics of plantations [43]. Therefore, MS and HS sensors are the most popular in PA, although they are expensive. Moreover, thermal sensors are used to collect temperature information. Their use proves to be optimal in irrigation-management applications [43]. The specific targeted application type is what dictates the adequate selection of sensors. For instance, MS sensors are suitable for the detection of diseases, offering many bands that can detect the sensitivity of symptoms. On the other hand, one RGB camera should be enough for data collection related to agricultural mapping.

The most well-known applications of UAVs for PA, as found in the literature, include weed mapping and management [44], irrigation management [45], crop spraying [46], vegetation health monitoring and diseases detection [47], and vegetation growth monitoring and yield estimation [43]. UAVs acquire information that can be useful for the measurement of different parameters such as the crop height and the Leaf Area Index (LAI), allowing crop growth control in crops such as cotton, wheat, or sorghum. UAVs can be used to calculate the most common vegetative index to determine the diseased tissue, i.e., the normalized difference vegetation index (NDVI), which is useful for monitoring crop health and detecting diseases at an early stage, also mapping the size of the defect. In addition, a very important field of UAV application is water management, as precision irrigation techniques improve the efficiency of water use resources [43]. UAVs have rapidly evolved into a common tool to increase agricultural output and overall efficiency, decreasing expensive inputs of water, fertilizers, and pesticides. They can also indicate damages in crops that cannot be easily detected from the ground and be applicable in areas of big dimensions [48].

Finally, spaceborne remote sensing satellites equipped with digital RGB cameras and MS or HS sensors are nowadays among the most widely used technologies in monitoring field variability, which usually includes landscape monitoring, yield, field, soil and crop variability, or variability due to anomalous factors [49]. By measuring the reflectance of the light incident on soil and crops, they are used to assess their characteristics and behavior by acquiring information at different spatial, spectral, radiometric, and temporal resolutions. Similarly to UAV-based remote sensing, information acquired from satellites is usually expressed by indices, among which the NDVI index is one of the most widely used. Each index is calculated from values at visible and non-visible wavelengths: red, green, blue, near-infrared, red edge, and infrared bands are the most frequently used ([12,50]).

Every satellite and sensor is characterized by different spatial and temporal resolutions. Temporal resolution is associated with the satellite itself and can be considered as the time the satellite takes to complete an orbit and revisit the same observation area. Sensors, instead, can have high spatial resolution and tend to have small footprints, or they have low spatial resolution and tend to have larger footprints. As Vanguard 2 and TIROS 1 were launched in 1959 and 1960, respectively, and are used for assessing meteorological information, the history of satellites for agricultural use starts in 1972 with Landsat 1 (1972–1978), a satellite capturing multispectral data for earth-surface image acquisition. After that, a series of Landsat satellites (from 2 to 9) were launched. Used in many parts of the world, these satellites (Landsat-7, -8, and -9 are still active) provide high-quality images in order to classify land uses, monitor crop conditions, and estimate irrigation water requirements, resulting in more affordable imagery than the aerial photography once used to classify land use across large regions. Later, from the end of the 1990s to the 2010s, other satellites were launched, such as IKONOS (1999–2015), which provides 4 m spatial resolution images; Worldview-2 (2009–present); and GeoEye-1 (2008–present), with a ≤2 m resolution; Sentinel-2A (2014–present) and -2B (2015–present), with a 10 to 60 m resolution; and other satellites constellations such as Pleiades-1A (2011–present) and -1B (2012–present), SkySat-1 (2013–present) and -2 (2014–present), or Superview-1 (2016–present), namely small satellites with compact, cheaper, and more replaceable sensors [51]. The primary benefits of this type of technology use are widely documented in the literature and include reduced environmental impact, increased crop yield, enhanced product quality, and input savings ([52,53,54]). Despite the number and variety of satellite data being made available at various costs and for different purposes, there are certain challenges to using satellite imaging.