Applications of Green Agricultural Technologies (GAT) and Sustainable Analytical Methods (SAM) in Precision Animal Agriculture and Product Quality Testing

Haruna Gado Yakubu; George Bazar; Tamas Toth

doi:10.5772/intechopen.1011165

Abstract

Modern agricultural development is primarily based on applying environmentally friendly and efficient technologies to deliver farm productivity. This chapter examines the advancements made in green agricultural technologies (GAT) and sustainable analytical methods (SAM) for precision animal production systems. The application of Unmanned aerial vehicles (UAV) enables real-time monitoring of pasture fields, animal behaviour and health. The application of NIR in the dairy industry has revolutionised feed and milk quality analyses, by providing real-time, accurate and precise nutritive and quality feedback. Smart feeding and milking have ensured efficient feeding and milking of a large number of animals within short periods. These technologies have significantly improved the efficiency of livestock production systems, enhanced ecosystem integrity, reduced drudgery, improved animal welfare and physiological conditions, and increased farmer income. Notwithstanding, the complexities involved in the application of some of the technologies and the high cost of almost all of them are hindering their adoption in low-income countries. Stakeholders and policymakers must collaborate effectively to make these technologies affordable in low-income countries, thereby enhancing precision livestock agriculture worldwide. On the other hand, where GAT and SAM are available, many expect a self-evident agricultural revolution simply to be an investment. Professionals, however, must understand that all new technologies and methods also require knowledge and experience in application, not only in development. Currently, most of the portable technologies are operational in offline mode, while others require network connectivity (online) for efficient operation. Manufacturers and users must consistently review the performance of these technologies for the effective design and development of future generations of the devices.

Keywords

precision agriculture
green technology
digital sensors
rapid analyses
smart farming

Author Information

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Haruna Gado Yakubu
- Department of Animal Science, University of Cape Coast, Cape Coast, Ghana
George Bazar *
- ADEXGO Kft, Balatonfüred, Hungary
Tamas Toth
- Agricultural and Food Research Centre, Széchenyi István University, Győr, Hungary

*Address all correspondence to: bazar@agrilab.hu

1. Introduction

The role of green agricultural technologies (GAT) and sustainable analytical methods (SAM) cannot be overemphasised in current agricultural development conversations, more so when current agricultural activities are climate and data-driven (smart agriculture) [1]. The global human population is estimated to be 9.8 billion by 2050 [2], coupled with a predicted worsening global climate [3]. Urgent steps and actions must be taken to sustain the production of animals to meet growing demands for animal protein [4]. However, the steps and actions must be designed and aimed at reducing the ecological footprint associated with current animal production systems [5]. The acquisition and utilisation of agronomic data to make informed or efficient decisions to boost or sustain animal production in the current smart agriculture era has sparked intensive development and advancement of some effective tools of green agricultural technologies (GAT) [6].

The application of GAT tools such as remote or digital sensing, robotics and big data technologies in animal farming has reduced the variable cost of production, increased efficiencies, improved animal welfare, and increased productivity per land area [7].

Related technologies referred to as sustainable analytical methods (SAM) have also been developed to reduce the labour intensiveness of conventional analytical methods, the environmental hazards associated with reagents, the time and cost of analyses, and improve the accuracy and precision of test results of animal feed and products, such as meat, milk and eggs. SAM tools such as near-infrared (NIR) spectroscopy, hyperspectral imaging, and machine olfaction or volatiles-sensitive sensor technology have revolutionised the science of feed and animal product quality testing.

The effective combination of GAT and SAM techniques in animal production systems to improve production, enhance management and welfare, and increase farmer profitability while minimising destruction to the ecosystem encompasses the concept known as precision animal farming or agriculture [8].

The aim of this chapter, therefore, is to present to readers the current advances made with GAT and SAM to advance precision animal production systems.

2. The concept of GAT and SAM in modern agriculture

2.1 Green agricultural technologies (GAT)

As a term or concept, GAT, also known as clean technologies, can be defined as technological tools in modern agriculture that work to reduce the negative impacts of farming activities on the environment [9]. GAT has become an important sustainable strategy for solving the global climate challenges confronting agriculture [10]. The application of GAT tools in the implementation of sustainable agricultural development strategies has led to the utilisation of animal resource efficiency and reduced environmental or ecological degradation [11]. At the global policy level, GAT falls within three main United Nations Sustainable Development Goals (UNSDGs), that is, goal 2: end hunger, achieve food security and improved nutrition and promote sustainable agriculture; goal 13: take urgent action to combat climate change and its impacts; and goal 15: protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss [12].

GAT tools used in precision animal farming include remote sensing (RS) [13], drone technology (DT) or digital imaging (DI) [14], which are mostly used to remotely gather agronomic information on pasture lands, animal growth data, robotics [15] for smart feeding, milking and care for animals, and digital sensor technologies (DST) [16] for control feeding.

2.2 Sustainable analytical methods (SAM)

The advocacy for more environmentally friendly analytical methods birthed SAM, which are rapid or correlative analytical methods that employ digital sensor technology and machine learning techniques to evaluate animal farm inputs and products that would have ordinarily been done with conventional methods [17]. They are sometimes referred to as green analytical methods (GAM) because their application mostly avoids environmental destruction [18]. SAM techniques, mostly, do not use reagents, are non-destructive or non-invasive, provide real-time data or results, are sensitive, precise, and accurate, and are generally relatively cost-effective in the long run when compared with conventional or wet chemistry methods [19]. Some of the most effective SAM technologies, mostly used in precision animal agriculture, include (i) near-infrared (NIR) spectroscopy, which has been largely accepted worldwide as an effective feed analytical method that provides real-time nutritive data on farm inputs like animal feed, and products such as meat and milk [19], (ii) [19] hyperspectral imaging which employs spectroscopy and digital imaging to provide valuable quality information on animal products [20] and (iii) machine olfaction or volatiles-sensitive sensor technology, which is effectively complementing human sensory tests aimed at evaluating animal products’ smell or aroma [21].

SAM technologies can be recognised as tools that record the digital chemical fingerprint of substances. In some exploratory approaches, the chemical fingerprints are used only, and differences of samples or sample sets are described based on the variations found in the NIR, hyperspectral, or digital smell data, solely. However, in most applications, multivariate data recorded with SAM technologies are translated to predict quantitative or qualitative properties. Figure 1 shows the general theory of development and application of SAM technologies.

Figure 1.
Schematic flow chart of the development and application of sustainable analytical methods.

At the development stage, combined input data is required: (i) multiple samples must be measured with both the SAM fingerprinting technology and (ii) reference data originating from, for example, laboratory measurements or sensory panel tests must be collected for each sample. The reference data may include continuous or discrete variables. Continuous variables can describe the chemical composition and physical or sensory attributes, while discrete variables can define the group identity within the sample set. There might be a direct or indirect relation between the continuous and discrete variables of a sample set; however, the definition of the data type is crucial: continuous variables are used for quantitative calibrations, and discrete variables are used for qualitative classifications. After recording the chemical fingerprints and the reference data of a bunch of samples, correlative approaches can take place when the pairs of fingerprints and reference data of each sample are compared, employing various mathematical-statistical modelling techniques. The paired data of many samples is collected into a large database. Development steps include data processing to enhance or reduce various signals, reducing the dimensions of multivariate data, and exploring basic variations. Then, multivariate data analysis is done using linear and non-linear calibration or classification modellings [22]. Both the raw data and the developed mathematical calibration and prediction models can be stored on local computers or in cloud servers. Users will then measure their new samples with the fingerprinting SAM technology and have the recorded chemical fingerprint evaluated by the previously developed quantitative or qualitative models, predicting the compositional variables, physical or sensory attributes, or group identities. With most SAM technologies, the whole process of data recording and prediction can happen in fractions of the time normally needed for reference measurements. The aim is to get accurate approximations and to exploit short response time. Many of the instant decisions taken based on information gathered from applications of SAMs could never be taken with conventional analytical methods since those measurements require considerable time, not considering the transportation and sample handling issues.

Since SAMs are correlative technologies, the accuracy of both the fingerprint and the reference data is crucial to improving prediction reliability. Fingerprints do not measure chemical compounds directly; instead, these are interpreted by mathematical models derived from the training dataset. Based on the previously experienced patterns of relation of fingerprint and reference data of the training database, predictions on quantitative or qualitative measures can be given for the new samples based on their fingerprints only. Due to the correlative approach, the training database must contain the sample types and variables of interest that may appear in the future. Additionally, to avoid inaccuracies arising from interpolative predictions, future samples to be measured and predicted must fall within the variance of the training database.

Contrary to targeted conventional chemical or physical methods, when one single variable is determined by one measurement, SIM technologies allow non-targeted analysis of samples through complex chemical fingerprints. This also means that one measurement can hold a wealth of information, and a holistic description of samples can be achieved: multiple compounds or both qualitative and quantitative measures can be determined simultaneously. Due to the non-targeted approach, SIM technologies are also excellent in monitoring and warning systems when the users do not know exactly what property of the product will change in the future. The general screening of the chemical fingerprint may reveal unexpected differences, drawing attention to errors or even excellence during product development.

The complex digital fingerprint, like the predicted variables, can be easily transferred and interpreted by various automated systems. Interestingly, unlike conventional targeted analytical methods, the archived fingerprints can be reassessed even when the sample does not exist anymore. For example, new information gathered in production recently may lead to the requirement of finding certain clusters amongst samples scanned long ago and describe patterns related to technological conditions in the production based on archived data – such recalls of saved data can assist in the refurbishment of technological lines, or identification of the period or cause of errors. Therefore, SAMs may provide data for high-quality assurance when the digital fingerprints of a batch can be recorded along the product chain. With blockchain technologies [23], these can be stored in a secure way and used for the benefit of the producer, processor, trader, and consumer.

Violating the basics described above would lead to challenged results and obscure conclusions; therefore, decent knowledge is required both in the case of the development and application of SAMs technologies. Besides the careful use of the technology and the results provided, users must always pay attention to the theory and practice of representative sampling [24]. Even the most precise technologies and most accurate calibrations can give false results on the investigated batches.

3. Some key GAT applications

3.1 Unmanned aerial vehicles (UAV) or drones in animal production systems

Unmanned aerial vehicles (UAVs) equipped with digital, satellite, or computer vision imaging technologies have been used extensively by ecologists and animal behaviour researchers to study the movement and habits of grazing animals in pasture and silvopasture systems, as well as in pastoral livestock production [25]. Drones with high-resolution or infrared cameras have been used for animal population count, tracking of group or herd movement and isolated animals on grazing fields (Figure 2a) [26, 27]. The technology has been used to study animal-preferred grazing plants on pasture fields with multiple or mixed crops [28]. In the application of fertiliser (foliar system) and pesticides to pasture crops at required dosages and times, drone technology has been very useful [29]. Natural mating behaviour amongst farm animals and conserved game animals has been successfully monitored with drones equipped with computer vision technology [30]. In recent years, drone and computer vision technology has been employed to identify sick, injured and springing heifers or pregnant cows that need to be assisted [31]. However, the technological challenges associated with UAVs include the high initial cost of purchase, adverse weather interference, especially on foggy days, and regulatory compliance or security clearance, which is mostly required before flying the drone [29].

Figure 2.
Schematic representation of green agricultural technologies (GAT): (A) unmanned aerial vehicles, (B) automated feeding, and (C) automated milking.

3.2 Automated or robotic animal feeding

The provision of sufficient and timely feeding, along with the genetic makeup of the animal and a good environment, form the basis for animal growth and development [32]. Before the invention of smart or intelligent feeding, manual feeding was the primary method of delivering feed to farming animals [33]. However, emerging challenges in the so-called “Global North”, such as an ageing population, labour shortages, and the liberalisation of agricultural markets, have led to the fast adaptation of intelligent or smart farming technologies in animal agriculture [34]. For example, manual feeding systems are challenged with high labour costs, contamination, and inefficient feed management or handling [33]. Smart engineering has given rise to automated or robotic smart feeding systems designed to improve feeding efficiency [35]. The technology consists of an automatic feed dispensing system, an unmanned ground robotic vehicle, and an embedded vision technology that delivers feed to animals within specified time intervals (Figure 2b). The ground robotic vehicle receives feeds from the dispensing system for delivery at the animal pen house in real time [36].

Sinnott et al. reported that when the manual feeding system was compared with the robotic system in a calf feeding trial, the automated system was 39% more efficient than the manual system [33]. Muir et al. [37] reported an improvement in recent robotic systems that have the technology to measure animal feed intake. Recent technologies have added water delivery components to the known feeding systems for multipurpose delivery [38]. Despite their usefulness, most smart feeding technologies are not yet fully automated, as human intervention is still required to load feed into the robotic system. Furthermore, the designs of most farms make it difficult for the robot to navigate successfully [39], thus requiring costly infrastructure modifications to meet the needs of modern technology.

Again, in novel combined systems, the technology can check the animals’ behaviour and the rearing and feeding conditions continuously. Big data systems equipped with AI technologies are evaluating the effect of various conditions on animal behaviour, physiology, and production. When a physiological or production deficit is detected, the system modifies the conditions to achieve optimal values in the control criteria. The method mimics the good farmer’s approach, when the conditions of the farm animals are evaluated very often before feeding, that is, during manual feeding [40]. Despite their advantages, these novel systems must be populated with appropriate input data and trained by experts to recognise patterns indicative of preclinical health issues and emerging problems in livestock.

3.3 Automated milking systems in dairy production

Milking technologies have also evolved from when a handheld pump was used, starting in the 1860s, to the automatic milking systems that appeared on the scene in the 1990s [41]. Current engineering advances have developed more robust and intelligent systems that can handle milk interval regularity and teat cup failures (Figure 2c) [42]. Robotic or automated milking systems help reduce bacterial contamination that may result from human contact, thus ensuring cleaner and safer milk [43]. Automated milking systems also ensure the maximum yield of milk harvested from large herds. It also eases the drudgery that is mostly associated with hand or conventional milking systems [44]. Some of the milking robots have embedded quality testing devices that enable the continuous monitoring of milk quality parameters or metrics, enabling early quality problems [45]. Again, the design of this technology is mostly done having the comfort or welfare of the dairy cow in mind, thus reducing emotional stress that is mostly associated with the process of manual milking [46, 47]. The challenges with the system are the initial cost of installation and the frequent engineering challenges that may require the presence of maintenance personnel from the source company [48]. There are also challenges with animals’ body size or confirmation score, udder shape and size, since the lack of homogeneous herds increases the chances of technological flaws [49]. This leads to the need for investment to improve genetics and rearing conditions.

4. Some key SAM applications

The evolution of biometric sensors and big data in precision livestock farming has been phenomenal. Digital sensor technologies (DSTs) are mostly non-destructive and, in animal agriculture, may be used to monitor animals’ health and behaviour in real-time rapidly, which enables farmer managers to integrate the acquired data for population-level management decision-making [16] through big data analytics systems. DSTs are mostly applied in SAM to ensure real-time results’ precision and accuracy. This section will focus on the applications of digital sensors such as NIR spectroscopy and hyperspectral imaging and machine olfaction or volatiles-sensitive sensor technology (Figure 3).

Figure 3.
Examples of sustainable analytical methods: (A) near-infrared spectroscopy: (i) benchtop, (ii) portable or handheld, (iii) NIR-on-chip spectrometers; (B) hyperspectral imaging: (i) whisk broom or point scanning, (ii) push broom or line scanning, (iii) snapshot imaging; (C) machine olfaction: (i) gas sensor array, (ii) biomimetic optoelectronic e-nose, (iii) gas chromatography-based virtual sensor array technology.

4.1 The applications of NIR spectroscopy in precision animal farming

One of the widely used or applied DSTs is the NIR spectroscopy. NIR is a correlative rapid analytical method that utilises the NIR region (780–2500 nm) of the electromagnetic spectrum to evaluate the nutritive qualities of animal feed and products such as meat, milk and its derivatives and egg [19]. The interaction of the NIR light with the organic bonds present in feed or animal products [50, 51] provides essential quality information about the investigated substance [52]. The NIR technique has evolved from benchtop devices to handheld and now pocket-sized or chip-embedded devices (Figure 3a) [53]. The application of the NIR technology in agriculture has been largely credited to Karl Norris [54, 55] for his role in the first analysis of egg quality and protein contents in soybean and wheat. Since then, NIR has been used for instant analysis of the nutritive value of feedstuffs such as corn [56], mixed crop silages [57], and soybean [58], and complete feed or total mixed ration [59], very essential for precision animal feeding. The output or product quality analysis with NIR not only allows for the nutritive evaluation of animal products but also gives valuable insight into product quality that is essential in grading and sorting for various market demands. The integration of NIR in grading systems allows for the sorting of eggs, for example, [60] reported the success of sorting double-yolk eggs from single yolks using NIR spectroscopy. Wold et al. reported the application of NIR to sort chicken breast meat into classes (normal fillets, wooden and spaghetti) based on breast muscle quality [61]. The evaluation of milk quality based on protein and fat levels [62] and bacterial spores’ presentation in the milk of dairy cows suffering from mastitis [63] have been extensively studied using NIR spectroscopy.

4.2 Hyperspectral imaging (HSI) and precision animal agriculture

The HSI technique combines spectroscopy with high-resolution imaging, which allows the collection of detailed information about the composition and characteristics of materials across the electromagnetic spectrum to acquire the spectrum for each pixel in the form of an image, which is mostly difficult with conventional imaging technology [64]. The technology has evolved with different miniature and low-cost hyperspectral sensors, such as Headwall Micro-Hyperspec, Cubert UHD 185-Firefly, and sophisticated hyperspectral sensors, such as PRISMA, DESIS, EnMAP, and HyspIRI [65]. Spatial and spectral resolutions may differ, while differences in the imaging technologies (e.g., snapshot, push broom scanning, and whisk broom scanning) may also lead to differences in the recorded data (Figure 3b) [66].

In precision animal agriculture, HSI technology has been used to assess pasture seed quality [67], to map nutrient concentration and deficiency in pasture fields [68, 69] to evaluate pasture quality and degradation status [70, 71] and to predict the nutritive and digestibility components of grass pasture [72]. In the area of animal product quality analysis and assurance, HSI has been used to analyse meat quality parameters [73], for example, for in-line sorting of red meat based on colour [74] for the classification of meat from different animals [75], monitoring of meat water holding capacity (WHC) [76], and identifying microbial-infected meat [38].

Though HSI has enormous potential in precision animal farming, the complexity of the technology is derailing its adoption even at the top industrial level. More effort is needed to simplify its application to the basic level of the agriculture value chain.

4.3 Machine olfaction: A promising tool for the rapid evaluation of animal feed and products

Another digital sensor technology with enormous potential in precision animal farming is machine olfaction, also known as volatiles-sensitive sensor array technology and popularly referred to as the electronic nose (e-nose) system. E-nose systems are mostly made of metal oxide semiconductor sensors (MOS), metal oxide semiconductor field-effect transistors (MOSFET), conducting polymer composites and quartz microbalance (QMB), biomimetic optoelectronics with colorimetric sensor array, and gas separation technologies combined with flame ionisation detectors (FID) (Figure 3c) [21]. The interaction between volatiles from feed and animal products with the sensors provides signals or smell fingerprints that are informative enough for quality evaluation based on the aroma pattern. The smell or odour of feed influences the feed intake of an animal [77]. Therefore, in recent years, attempts have been made by silage scientists to screen silages based on the smell pattern generated because of fermentation [78, 79]. The discrimination of bovine milk as affected by feeding using e-nose has also been well documented [80, 81, 82]. The technology has also been used to study the shelf life of milk [83, 84]. Meat quality analysis as influenced by feeding has also been comprehensively studied [65, 85, 86]. The technology is still developing, and hopefully, the invention of portable e-noses by industrial experts will help in the advancement of the technology in precision animal farming [87]. Table 1 shows a summary of some of the most applied GAT and SAM technologies in the advancement of precision animal agriculture.

Technology	Type	Application	Author
	UAV	For animal population count, tracking herd movement and isolated animals on grazing fields	[26, 27]
	UAV	For monitoring animal-preferred grazing plants on pasture fields with multiple crops	[28]
GAT	UAV	In the application of fertiliser (foliar system) and pesticides to pasture crops at the required dosages and times	[29]
	UAV	For monitoring natural mating behaviour amongst farm animals and conserved game animals	[30]
	UAV	To identify sick, injured and springing heifers or pregnant cows that need to be assisted	[31]
	Smart feeding	Robotic system applied in precision calf feeding	[33]
GAT	Smart feeding	A recent robotic system to measure animal feed intake	[37]
	Smart feeding	Recent technologies have added water delivery components	[38]
	Smart milking	Automated milking systems for maximum yield and to reduce bacterial contamination of milk	[43, 44]
	NIRS	Instant analysis of the nutritive value of feedstuffs (corn, soybean, and silages)	[56, 57, 58]
	NIRS	Integration of NIR in grading systems allows for the sorting of eggs	[60]
SAM	NIRS	The application of NIR to sort chicken breast meat into classes (normal fillets, wooden, and spaghetti)	[61]
	NIRS	Evaluation of milk quality based on protein and fat levels	[62]
	NIRS	Detecting bacterial spores’ presentation in the milk of dairy cows suffering from mastitis	[63]
	HSI	In assessing pasture seed quality	[67]
SAM	HSI	In the mapping of the nutrient concentration and the deficiency in the pasture fields, the pasture quality and the degradation status	[68, 69, 70]
	HSI	To analyse meat quality parameters, such as in-line sorting of red meat based on colour,	[74, 75]
	HSI	classification of meat from different animals, monitoring of meat water holding capacity, and identifying the microbial-infected meat	[38, 76]
	e-nose	Screening silages based on the smell pattern generated because of fermentation	[78, 88]
	e-nose	Discrimination of bovine milk quality as affected by feeding	[80, 81, 82]
SAM	e-nose	To study meat quality (odour specific) analysis as influenced by feeding	[65, 85, 86]

Table 1.

Summary of some of the most applied GAT and SAM technologies in the advancement of precision animal agriculture.

GAT: green agricultural technologies; SAM: sustainable analytical methods; UAV: unmanned aerial vehicle; NIRS: near-infrared spectroscopy; HSI: hyperspectral imaging.

5. Conclusion and prospects of GAT and SAM

Based on the literature cited in this chapter, the following key conclusions can be drawn:

The application of GAT and SAM has improved the efficiency of animal farms, enhanced safer food through quality assurance, improved animal welfare and physiological conditions, and increased the profitability of farmers and the protection of the agricultural ecosystem.
Current GAT and SAM technologies are expensive for low-income farmers, making it very difficult to adopt in low-income countries.
Though efficient and effective technologies come with high costs, experts must focus on producing relatively cheaper technologies with the same level of efficiency for low-income countries to enable the continuous supply of food to their populations, preventing starvation and preserving ecosystems.
Stakeholders involved in the promotion of GAT and SAM must influence policymakers to adopt these technologies within the framework of sustainable animal agriculture.
The practical application of GAT and SAM in animal production can sometimes be very complex, especially in remote areas where the technology may need network connectivity to work efficiently. Though some of the portable technologies are currently efficient in offline mode, some require network connectivity (online) for efficient operation.
Manufacturers and users of GAT and SAM must consistently review the performance of these technologies for effective design and development of future generations of the devices.
An example is hyperspectral imaging (HIS) technology: more needs to be done to improve the easy applicability of HIS in the animal product quality testing field.

Conflict of interest

The authors declare no conflict of interest.

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