Smart Farming and Sustainable Agriculture

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Table of Contents

Abstract
Introduction
Background
What Is Smart Farming
Smart Farming in Detail
Future Scope
Conclusion
References

Abstract

The drive toward efficient yet sustainable agriculture in the contemporary world necessitates the adoption of smart farming. Smart farming refers to a set of solutions that increase agricultural efficiency, mainly through the use of information and communication technologies.

Data acquisition, data evaluation, and precision application allow improving yields while minimizing the losses. The Internet of Things is of particular use in smart farming, as it makes managing the farm’s assets much easier and also allows creating highly automatized solutions. Blockchain is another promising technology since it not only facilitates safe storage and sharing of information but also contributes to creating a democratically organized international farming community. Increased robotization and interoperability are among the solutions that will likely shape smart farming in the foreseeable future. As a result, smart farming has the potential to revolutionize the world’s agriculture in the years and decades to come.

Introduction

Considering the rapid growth of the Earth’s population, the problem of sustainable agriculture being able to feed the ever-increasing number of people remains an issue of current interest. However, the opportunities for extensive agriculture expansion are mostly exhausted, which means that the future of agricultural cultivation should look for intensive methods. One of the ways to intensify agriculture and make it more effective overall is precision cultivation that tailors agricultural decisions with regard to the site and the type of crops.

Precision cultivation depends to a high degree on the use of digital technologies in agriculture, which is generally defined as smart farming. Smart farming allows for a wide range of options, from robotization and satellite imagery to the Internet of Things and the blockchain technology that increases the efficiency of crop cultivation by optimizing the use of resources. Providing data acquisition, data evaluation, and precision application with the help of the Internet of Things and blockchain, smart farming may impact agriculture all over the world and has immense potential for the future.

Background

In the coming years, one may expect precision cultivation to have a considerable impact on agricultural businesses, large-scale and small-scale organizations alike. Different countries adopt smart farming methods in their agricultural production to address the issues they confront. As of 2016, the country producing the highest number of scholarly publications on smart farming was China, with 31.84 percent of all academic texts in the field (Pivoto et al., 2017). The USA was responsible for 8.94 percent of scholarly publications, South Korea – for 8.38 percent, and Germany and Japan accounted for 6.15 and 5.59 percent, respectively (Pivoto et al., 2017).

Thus, the world’s nations are interested in smart farming regardless of the differences in particular conditions of agricultural cultivation in their respective countries. For instance, the most significant concern for South Korea is maximizing the efficiency of cultivation for its small amount of arable land (Pivoto et al., 2017). India, as a country expected to suffer from water scarcity, is most interested in adopting smart watering systems (Gupta, Mishra, Bogde, & Kulat, 2016). These examples demonstrate that smart farming makes it possible to address numerous and varying problems all over the world.

What Is Smart Farming

The term “smart farming” refers to using information and communication technologies (ICT) to enable precision agriculture with greater accuracy and regardless of the field’s size. Balafoutis et al. (2017) define precision agriculture as “the management of spatial and temporal variation in the fields with regard to soil, atmosphere and plants” (p. 22). The idea of precision cultivation is not new, as small farms aim to tailor their efforts for each particular crop in each specific field since time immemorial. However, the old-style precision cultivation relies mostly on the farmer’s personal inspection of the field and draws on individual experience, which makes it inapplicable on a greater scale.

This limitation is why smart farming technologies are immensely important for enabling precision cultivation regardless of scale. Smart farming is, therefore, farming that relies on a set of technological solutions “drawing from research in precision farming, farm management information systems (FMIS) and agricultural automation and robotics” (Balafoutis et al., 2017, p. 23). The variety of technologies that fall under the term allow smart farming to address many different tasks involved in crop cultivation.

Smart Farming in Detail

Data Acquisition, Data Evaluation, and Precision Application

Data acquisition technologies represent the first type of smart farming technologies that enable increasing agricultural productivity to a considerable extent. This category includes solutions designed to measure and record crop and field characteristics. One of the most common uses of data acquisition technologies is mapping, achieved through either satellite imagery or unmanned aerial vehicles (UAVs) (Tripicchio, Satler, Dabisias, Ruffalsi, & Avizzano, 2015).

Mapping allows the farmer to quickly and relatively assess the patterns that would be much harder to notice if inspecting the fields on foot. Another type of data acquisition is assessing environmental properties, such as the normalized difference vegetation index (NDVI) or soil moisture, which enables the farmer to monitor the state of crops and soils constantly and efficiently (Balafoutis et al., 2017). Apart from that, data acquisition technologies also include global navigation satellite systems (GNSS) used in the smart farming machinery, which is often autonomous (Balafoutis et al., 2017). In general, data acquisition in smart farming allows making more informed decisions with regards to fields, soils, crops, and machines used in agriculture.

Another possible application of smart farming technologies is data evaluation since the data gathered may only be of use if one analyzes it accurately and makes correct conclusions. One of the ways in which technologies can help the farmer in this respect is management zone delineation, which allows defining “parts of the field with common characteristics that can be managed separately” (Balafoutis et al., 2017, p. 45).

Another possible use of smart farming technologies for data evaluation is decision support systems, which, as follows from the name, inform the farmer’s decisions regarding farm management. Some of such solutions offer “comprehensive and scalable analysis, recommendation/visualization, or sharing of crop performance data among farmers, growers, biologists, government, and commercial organizations” (Jayaraman, Yavari, Georgakopoulos, Morshed, & Zaslavsky, 2016, p. 2). Hence, smart farming not only provides a broad range of basic information regarding the fields and crops but also facilitates decision-making based on this information.

Finally, the third aspect of smart farming technologies is a precision application, which refers to the high-accuracy digitized performance of numerous agricultural tasks. For instance, a wireless network, including temperature and moisture sensors, allows implementing precision irrigation that would address the needs of the crops without wasting any water (Viani, 2016). Another example is the variable-rate “tillage and fertilizer application machinery” that enables distributing granulated fertilizer, lime, manure, and pesticides to a precisely needed degree (Nhamo, Chikoye, & Gondwe, 2017, p. 17).

Since, for instance, under-application of lime may cause considerable yield losses, but its over-application leads to problems with specific nutrients, applying it to an accurate degree is immensely important (Balafoutis et al., 2017). Apart from that, the auto-guidance also increases farming efficiency, as it allows the farm machinery “to follow straight lines to reduce overlaps of the tractor and equipment passes” (Balafoutis et al., 2017, p. 58). Therefore, smart farming technologies open the possibilities for highly efficient use of available resources with maximum productivity.

Internet of Things in Smart Farming

One of the particularly notable technological solutions in smart farming is the Internet of Things (IoT). IoT refers to digitized networks of physical objects, each of which possesses a unique identifier. When applied to agriculture, IoT enables real-time monitoring of large numbers of units, which may prove especially useful in animal husbandry. For instance, in Australia, it is mandatory to “affix passive RFID ear tags to their cattle and to report movements between farms to an online national database” (Kamilaris, Gao, Prenafeta-Boldu, & Ali, 2016, p. 442). As a result, IoT allows managing the farm’s assets with greater precision and efficiency, and with a fraction of effort, it would have required otherwise.

However, IoT displays its true potential in smart farming not when applied to animal husbandry, but when used to interconnect the numerous pieces of machinery involved in the cultivation process. It was already mentioned above that smart farming technologies allow addressing a broad range of tasks, from mapping and moisture measurement to watering and variable rate distribution of fertilizer, lime, manure, and pesticides.

Yet for all the advantages offered by the machines performing these tasks, each of them only realizes a fraction of its potential if used independently, as each has to be put to action manually. IoT frameworks, on the other hand, allow integrating the smart farming machinery to create highly automated digitized frameworks sharing and implementing the information in real-time.

For instance, IoT enables combining “data from a fertilizer sprayer on a tractor… with the data obtained from soil moisture sensors” to make automated decisions informed by both (Jayaraman et al. 2016, p. 2). Taken separately, the pieces of smart farming machinery are only tools for solving specialized tasks, but when united via IoT framework, they form a highly effective and automated network addressing the crop needs in real-time.

Blockchain in Smart Farming

As noted above, smart farming is mainly based on ICT, which means it invariably involves data sharing on a grand scale. Jayaraman et al. (2016) point specifically to the “enormous velocity of data” generated, stored, and shared when applying smart farming in practice (p. 3). The necessity to safely operate large amounts of information produces a demand for the corresponding data security measures, and blockchain technology may be of great use in this particular respect. Lin et al. (2017) remind that the weakest link in any data safety system is people whose biases usually constitute “the most important factor affecting objective prioritization” (p. 2).

These biases create a potential for unscrupulous manipulation of smart farming databases. For example, governments may bias large agriculture systems “to maximize inexpensive food supplies from rural agriculture to urban infrastructure, where the majority of elected officials’ constituents reside” (Lin et al., 2017, p. 2). Blockchain technology, on the other hand, distributes database management among multiple actors, thus reducing the likelihood of any system-wide data manipulation (Lin et al., 2017). Therefore, the application of blockchain allows addressing data safety concerns inevitably involved in smart farming.

Yet one should also be aware of another advantage of blockchain technology in smart farming: using it fosters a network of co-dependent actors that functions democratically. As mentioned above, centralized ways of storing and sharing data, such as centralized and stringently regulated networks or even, to some degree, cloud computing enable the manipulation of data (Lin et al., 2017, p. 2).

By eliminating this potential, the networks created through the use of blockchain technology – as opposed to those with central authority governing information sharing and distribution – contributes to community building. Blockchain empowers the users to regulate the information themselves instead of putting their trust in centralized authority, and, as a result, one cannot deny its “contribution to digital democratization” (Lin et al., 2017, p. 9).

More importantly, still, the users themselves recognize this advantage and stress that creating an international community of farmers is an essential outcome of smart farming. One of the farmers interviewed by Carolan (2018) emphasized that sharing information was “about building social networks, building communities” (p. 754). Blockchain technology allows building these communities democratically, which is yet another positive influence on farming.

Future Scope

As a rapidly developing field that answers the essential challenges of its time, smart farming has highly promising perspectives, and one of them lies in the increased robotization of agriculture. Among other robots, UAVs demonstrate the potential to generate the highest income due to their effectiveness in solving their tasks. Employing the UAVs enables the farmers to use their fields with higher efficiency for a better overall outcome.

For instance, it helps in delineating management zones with different soil characteristics, which, in turn, allows decreasing the plowing depth by changing plowing techniques correspondingly (Tripicchio et al., 2015). This is only one example of how increased robotization may make farming more productive and sustainable at the same time – and, considering this, robotization is a priority for smart farming in the foreseeable future.

Another aspect of smart farming, which is of even greater importance for the future as it promises potentially immense advantages, is ensuring interoperability between different devices and machinery used. As of now, there are ready solutions for smart farming that – thanks to the IoT – use information sharing between different devices in real-time (Jayaraman et al. 2016), However, these solutions “can only utilize a small number of specific IoT devices” and also usually demand those to come from a specific producer (Jayaraman et al. 2016, p. 2).

Such an approach limits the number of options available to a farmer severely and also impacts the overall efficiency of the system negatively. For instance, it deprives a farmer of an opportunity to use cheaper or more effective sensors simply because these come from a different producer (Jayaraman et al. 2016). Hence, IoT solutions that would allow interoperability of devices and machinery regardless of the producing company have the potential to increase farming efficiency immensely and will be essential in the future of smart farming.

Conclusion

As one can see, smart farming allows data acquisition, data evaluation, and precision application with the help of the blockchain, and IoT may reshape the world’s agriculture in the future. Using information and communication technologies in farming provides for greater efficiency, as recognized by many nations from the USA and China to India and South Korea. IoT and blockchain are of particular importance, as they allow creating highly automatized autonomous systems, enable safe data sharing, and contribute to creating a democratic international community of farmers. As for future prospects, increased robotization and interoperability of hardware have the potential to make smart farming even more effective in the years to come.

References

Balafoutis, A.T., Beck, B. Fountas, S., Tsiropolous, Z., Vangeyte, J., Wal, T., … Pedersen, S.M. (2017). Smart farming technologies – Description, taxonomy, and economic impact. In S.M. Pedersen & K.M. Lind (Eds.), Precision agriculture: Technology and economic perspectives (pp. 21-77). Berlin, Germany: Springer.

Carolan, M. (2018). ‘Smart’ farming techniques as political ontology: Access, sovereignty and the performance of neoliberal and not-so-neoliberal worlds. Sociologia Ruralis, 58(4), 745-764.

Gupta, A., Mishra, S., Bokde, N., & Kulat, K. (2016). Need of smart water systems in India. International Journal of Applied Engineering Research, 11(4), 2216-2223.

Jayaraman, P.P., Yavari, A., Georgakopoulos, D., Morshed, A., & Zaslavsky, A. (2016). Internet of Things platform for smart farming: Experiences and lessons learnt. Sensors, 16(11), 1-17.

Kamilaris, A., Gao, F., Prenafeta-Boldu, F.X., & Ali, M.I. (2016). Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications. In Proceedings from 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) (pp. 442-447). Reston, VA: IEEE.

Lin, Y.P., Petway, J.R., Anthony, J., Mukhtar, H., Liao, S.W., Chou, C.F., & Ho, Y.F. (2017). Blockchain: The evolutionary next step for ICT E-agriculture. Environments, 4(3), 1-13.

Nhamo, N., Chikoye, D., & Gondwe, T. (2017). Smart technologies for sustainable smallholder agriculture: Upscaling in developing countries. London, United Kingdom: Academic Press.

Pivoto, D., Waquil, P.D., Talamini, E., Finocchio, C.P.S., Corte, V.F.D., & Mores, G.V. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21-32.

Tripicchio, T., Satler, M., Dabisias, G., Ruffaldi, E., & Avizzano, C.A. (2015). Towards smart farming and sustainable agriculture with drones. In Proceedings from In Intelligent Environments ’15: International Conference on Intelligent Environments (pp. 1-4). Prague, Czech Republic: CPS.

Viani, F. (2016). Experimental validation of the wireless system for irrigation management in smart farming applications. Microwave and Optical Technology Letters, 58(9), 2186-2189.

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