1. Introduction

Agriculture has played a key role in the global economy in recent years [1]. Estimates show that current agricultural production must increase 60–100 percent with everything else unchanged to meet the nutritional needs of a future human population of 9–10 billion. In addition, agricultural intensification over the last few decades has had negative environmental impacts [2]. As a result, the pressure on the agricultural system is greater than ever before [1]. In order to minimize these issues, traditional agricultural management methods have been complemented by new sensing and driving technologies and improved information and communication technologies (ICT) [3]. Based on the concept of “produce more with less” [4], precision agriculture, also known as precision farming or smart farming, has the potential to contribute to the wider goal of meeting the increasing demand for food whilst ensuring the sustainability of primary production, based on a more precise and resource-efficient approach to production management [5].

PA technologies are used in the important stages of the crop growth cycle (soil preparation, seeding, crop management, and harvesting). However, it is not just crop and fruit farming that has benefited from precision farming technologies—farmers engaged in livestock rearing are also experiencing the positive benefits derived from precision farming technologies [5]. PA could be divided into two categories: precision crop farming, which consists of the application of precision farming technologies to manage spatial and temporal variability for improving crop performance and environmental quality, and PLF, which is based on the use of advanced technologies to optimize the contribution of each animal. Through this “per animal” approach, the farmer aims to achieve better results in livestock farming [4]. Precision crop farming and PLF are currently being shaped by two major technological trends: big-data and advanced-analytics capabilities on the one hand, and aerial imagery, feeding and milking robots, and intelligent sensors, on the other [6].

The current paper aims to briefly review the recent scientific and technological trends of precision farming and its application in crop and livestock farming. This study can serve as a research guide for both the researcher and the farmer in applying technology to agriculture. The remainder of this paper is organized as follows. Section 2 presents precision crop farming including different farming activities such as soil monitoring, precision seeding, smart irrigation and fertilization, and grass yield monitoring. It is also an approach to farm machinery and its important role in precision farming. Section 3 presents the PLF and scientific and technological developments concerning animal behavior, welfare, feed and live weight measurement, and automatic milking systems. In Section 4 , slight considerations are made based on the risks and concerns inherent to precision farming. Finally, in Section 4 , we present the conclusions.

2. Precision Crop Farming

Precision crop farming or site-specific crop management is a concept based on sensing or observing and responding with management actions to spatial and temporal variability in crops. The “sensing” component of the concept is a fundamental element of precision crop farming [7]. Sensors in fields and crops are starting to provide granular data points on soil conditions as well as detailed information on climate, fertilizer requirements, water availability, and pest infestations. In addition, aerial images captured by non-aircraft vehicles, crews, or drones can patrol fields, alert farmers to crop maturation or potential problems, and provide early warning of deviations from expected growth rates or quality. Satellites can also be at the service of precision crop farming, facilitating the detection of relevant changes in the field by using satellite imagery [8]. In this section, we introduce some of the scientific research and technological developments involved with smart crop farming ( Table 1 ) as well as the important role of machinery in precision farming.

Soil electrical conductivity (ECa) sensors measure the soil solute concentration while assessing the soil salinity hazard [11]. Mobile measurements of ECa have become widely used to map soil variability. The greatest potential use of ECa scanning is in the survey of spatial soil variability and delineating potential site-specific management zones. This, in turn, would allow for better resource allocation and long-term management planning [10]. Soil water content sensors such as frequency domain reflectometry (FDR) or time domain reflectometry (TDR) sensors measure the amount of water (volume or mass) contained in a unit volume or mass of soil by using electrodes. It is expressed by the change in capacitance value, which depends on the dielectric constant of the soil. It can range from 0 (completely dry) to the value of the materials’ porosity at saturation [12]. The sensor must be calibrated for each location because the measurements depend on the type of soil [9]. Soil moisture content sensors (or “volumetric water content sensors”) such as tensiometers evaluate the soil water tension or suction, which is a denotation of the plant root system effort while extracting water from the soil. It can be used to estimate the amount of stored water in the soil or how much irrigation is required to reach a desired amount of water in the soil [13]. Soil moisture content can also be determined by Photodiode, an optical sensor that uses the light to measure soil properties, namely clay, organic matter, and moisture content of the soil [9]. Smolka et al. [14] recently presented a mobile sensor aimed for on-site analysis of soil sample extracts used to detect the primary plant nutrients in their available form, at a fraction of the time and cost associated with traditional laboratory soil analysis. The sensor was particularly appropriate for the analysis of NO 3, NH 4, K, and PO 4 and followed on from previous studies exploring the potential for on the-go soil sampling for nitrate using electrochemical sensor platforms and ion-selective electrodes [10]. There are also other types of sensors such as ground penetrating radar (GPR) and gamma ray spectrometry (GRS), which can be used through the ground cover vegetation. GPR data were correlated with soil hydrology parameters, and GRS data were related to some soil nutrients and other soil texture characteristics. Sensors based on optical reflectance as well as multi-spectral and hyperspectral sensors also have good correlation with soil properties [15,16]. Additionally, researchers from the EU-funded MISTRALE project have designed a system that can measure soil moisture from a drone flying at low altitude using Global Navigation Satellite Systems (GNSS) reflectometry. The system, still a prototype, can produce high-resolution maps of soil moisture by harnessing signals from either the Galileo or GPS global satellite systems. This could help farmers make better decisions about when and where to irrigate, and to help water managers understand weather events such as flooding and water logging [5].

In recent years, the literature has provided several studies on the optimization of irrigation water management. Ortega et al. [23] evaluated the effects of different temperatures on greenhouse tomato growth using an automatic irrigation system to suggest an optimal irrigation strategy for improving the IWUE. Goap et al. [24] proposed an IoT based smart irrigation system along with a hybrid machine learning based approach to predict the soil moisture with very encouraging results. The proposed algorithm uses the sensors’ data of the recent past and the weather forecasted data for the prediction of soil moisture for upcoming days. Furthermore, the prediction approach is integrated into a cost-effective standalone system prototype as it is based on open-source technologies. The auto mode makes it a smart system and it can be further customized for application specific scenarios.

Recently, Liao et al. [29] developed a smart irrigation system based on real-time soil moisture data to estimate the dynamic crop water uptake depth (WUD) using the spatiotemporal characteristics of soil moisture distributions. According to the authors, this study estimated the tomato WUD from the distribution characteristics of soil moisture in the profile, providing a real-time and effortless method to determine the dynamic designed irrigation depth for guiding irrigation events.

3. Precision Livestock Farming

Successful grazing and pasture management require an understanding of the adjustment mechanisms behind the grazing behavior [61] that enables adaptation to grazing conditions [62]. As well as facilitating the precise management of grazing, the monitoring of animal position, foraging, and other behaviors can bring considerable benefits for animal health and welfare by continuously monitoring each animal in the flock, any small deviation from ‘normal’ behavior (for that individual animal) can be quickly identified and flagged to the farmer [63].

Animal health is of key importance in the livestock industry as it impairs production efficiency through growth retardation or even mortality, animal welfare through pain and discomfort, and it can even impair human health through the misuse of antibiotics or zoonosis [72]. In fact, the large density of animals living so close to humans in some countries can transfer a high number of zoonosis diseases to humans [73]. The monitoring of health problems in the early detection of clinical signs of diseases on the farm is one of the key issues from which PLF has arisen [66]. Most diseases are easily treated when detected in an early phase, although prevention is always the priority [72]. Modern technologies such as sensors, big data, artificial intelligence (AI), and machine learning (ML) algorithms enable farmers to react to diseases after they become evident, or pro-actively using vet services, and also provide an opportunity to constantly monitor key animal health parameters such as movement, air quality, or consumption of feed and water. By constantly collecting these data and using advanced technology to predict deviations or abnormalities, farmers can identify, predict, and prevent disease outbreaks. Therefore, this technology has a significant cost advantage over older detection methods [71].

The implementation of automated feeding systems (AFS) can provide a cost-effective alternative to manual regimes. Feeding units have been developed for a variety of animal systems including cattle, sheep, and pigs. These systems can be advantageous by providing an interface that monitors time and date of feeding, the electronic identification of each animal, the weight of the feed consumed, and the duration of feeding [79].

The measurement of average live weight gain (speed of growth) of a distinct group of animals is one of the most important measurements to be undertaken on livestock farm as the speed of growth will affect both the financial performance of the farming enterprise as well as the final body composition of the animals [59]. Recent systems have appeared on the market (such as the Osborne Weight-Watcher™). Weighing systems based on image analysis techniques have been designed to determine the weight of individual or groups of animals (specifically pigs) with acceptable precision by correlating dimensional measurements of the animals to weight. Recent studies carried out by Banhazi et al. [82] have demonstrated that those systems can reliably provide a performance record of successive batches of animals and in a timely manner.

4. Risks and Concerns about Precision Farming

It is important to understand how farmers interpret the value of technology in the context of their farms. On one hand, farmers look at value to their farming business in the adoption of the usage of new technologies to solve future problems [73]. On the other hand, many producers perceive that adopting high productive management systems involves increased risk [54]. The perceived risks involve the risk of financial failure because of unforeseen environmental or market circumstances, damage to the farm infrastructure such as soils and pasture, compromises to animal health and welfare, and the risk of increased stress on them from managing an intensified system [54,56]. Another risk that precision farming shares with other technologies is the further consolidation of farms as far as wealthier participants in a sector can benefit the most from recent technologies [91]. There is also the concern about some instances where technology cannot be used effectively. In some cases, farmers are either reluctant or may not be able to use the latest technology on their farms. The selling of pre-mature technology to farmers by companies without sufficient trials or evidence could result in costly losses for the farmers, namely, when it comes to predicting epidemic diseases in large scale animal farms. Furthermore, use of the data is itself a problem. Vast amounts of data from the technology products and services get stored in remote cloud servers. This is often monetized for commercial benefits. Big corporations can now collect, use, and even sell data from farmers. The rising tension between corporations and farmers over data misuse is a considerable threat [71].

This entry is adapted from the peer-reviewed paper 10.3390/ani11082345