Technological innovations have been gradually integrated into the agricultural sector. This is particularly true for smart technologies that use automated machinery to collect, process, and analyze data to inform decision-making.
Smart technologies play increasingly valuable roles in agriculture, which will likely continue growing in the future, particularly with the need to develop more sustainable agricultural practices.
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Current limitations to sustainable farming in modern agriculture
The growth of human populations worldwide has continuously pressured food production to increase productivity over the last century. As a result, modern agricultural practices are typically characterized by monocultures requiring large spatial expanses relying on artificial fertilizers, pesticides, and insecticides. This has led to the widespread degradation of natural habitats, the loss of biodiversity, and the rise of greenhouse gas emissions.
Due to the consequences associated with agricultural practices, the projected reduction in the availability of arable land, the decrease in soil health, and growing food demands, modern food production cannot be sustained into the future. Although sustainability is often associated with organic, diverse, and environmentally friendly principles to the detriment of productivity, sustainable practices aim for a balanced productivity that does not compromise food production into the future. This important distinction has hindered the implementation of sustainable practices despite the ongoing and expected changes in environmental conditions.
Nevertheless, an element of modern agriculture that can improve sustainability and has largely escaped the dilemma of productivity versus environmental consideration is the integration of technological innovations. In particular, smart technologies have revolutionized agriculture. Smart technologies in agriculture currently encompass automated machinery and instruments used during food production, packaging, and transport, as well as machine learning algorithms to process and analyze data.
The main objective of smart technologies is to increase food productivity, whether for crops, livestock, or aquaculture. However, smart technologies can also readily integrate sustainable practices into food production. Specifically, smart technologies facilitate transitions towards new practices and have extensive adaptive potential in unpredictable environments, which aligns with policy goals, including the UN Sustainable Development Goals (SDGs).
The revolutionizing elements of smart technologies for agriculture
Modern agriculture is undergoing extensive transformations as smart technologies can change practices. The current transition away from manual labor and intensive external support towards automated processes and data-informed decisions is revolutionizing every sector of agriculture. Smart technologies are also being integrated across food production chains, from climate monitoring to seeding to packaging and transportation.
Smart technologies can transform agriculture, first and foremost, by increasing productivity. Automated machinery requires supervision and maintenance but few expenses aside from the initial setup. Machines can rapidly use collected data to adjust fertilizer, pesticide, or water usage in real-time, improving resource efficiency and making practices more sustainable.
For instance, in a 2021 review of smart farming applications by Mohammed et al., the authors present many successful applications. From automated unmanned aerial vehicles (UAV) that monitor crop growth to the use of robots that can harvest, plant seeds, detect weeds, and spray for pests all in one, smart farming is already being used extensively. More recent implementations, such as smart irrigation systems, wireless communications, and Smart Decision Support Systems (SDSS), which integrate several deep neural network systems to inform and execute managerial decisions, also demonstrate the capacity for technologies to become faster and more efficient in the near future.
Additionally, smart technologies provide greater adaptive capacity in the face of environmental change. Specifically, long-term data collection provides failproof systems that can track changes in food production and anticipate risks of drought, pests, or precipitation, by adjusting resource use (e.g., water, pesticides, etc.) accordingly. As a result, smart technologies may provide a useful framework with different tools that can carry out broader-scale policies aiming toward sustainable practices, such as the SDGs.
The revolutionizing capacity of smart technologies was further reviewed by Glaroudis et al., 2020, who discussed the digital protocols for data collection and management when using the Internet of Things (IoT). The authors offer performance indicators and comment on the IoT frameworks within smart farming. The researchers discuss how the efficiency of IoT in farming relies on nodes, gateways, and application servers to gather and analyze data and how these elements can improve future prospects but also limit the current applications of IoT in farming.
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Future considerations for smart farming
Although smart farming is a relatively new term in agriculture, many technologies are becoming increasingly common. As a result, the runway for smart farming to continue expanding remains extensive. This is primarily due to the interest in automated technologies in other sectors, such as digital, financial, and industrial fields, which can then be crossed over into agriculture.
Reviews of research efforts on regional scales, such as the one by Moysiadis et al., 2021, reveal the ongoing innovations in Europe and demonstrate the extent to which smart farming is becoming a priority for many nations in both private and public sectors of research. As a result, many regions are expanding more rapidly than others, which may lead to different rates of smart integration.
However, this may not damage other nations, as emerging technologies are becoming affordable faster. Moreover, research efforts also focus on standardizing practices to offer users the most efficient data usage. This is particularly true for implementing global policies, such as the SDGs, which advocate for fairer and more widespread uses of technologies.
Since the use of smart technologies in agriculture is relatively recent, the field may also undergo rapid transitions in the next decades. For instance, how we use automated data to inform decision-making may change. This was exemplified by Navarro et al. (2020), who found that data used to be applied more reactively to agricultural issues, but has shifted to fill a predictive role, particularly when diagnosing crop-related issues.
Research in smart farming remains in its infancy, and many potential trajectories would provide considerable progress for developing sustainable agriculture, from multi-technology integrations such as the Smart Decision Support Systems to the use of technologies across spatial, temporal, and biological scales. The latter was discussed by Idoje et al. in a 2021 review and included unsupervised learning algorithms to understand the physiological activities of plants under stress or decode the livestock voices during pain or in reaction to a sudden change within its environment.
Ultimately, smart technology offers extensive potential to develop sustainable agricultural practices. Predictions for future agricultural practices all include a greater role of smart farming, particularly when attempting to execute goals related to sustainable agriculture. From improving the use of resources to reducing waste, smart farming may already be an essential pillar for modern agriculture to increase productivity in a rapidly changing world.
Sources:
- Glaroudis, D., Iossifides, A., & Chatzimisios, P. (2020). Survey, comparison and research challenges of IoT application protocols for smart farming. Computer Networks, 168, 107037. https://doi.org/10.1016/j.comnet.2019.107037
- Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020). Security and Privacy in Smart Farming: Challenges and Opportunities. IEEE Access, 8, 34564–34584. https://doi.org/10.1109/access.2020.2975142
- Idoje, G., Dagiuklas, T., & Iqbal, M. (2021). Survey for smart farming technologies: Challenges and issues. Computers &Amp; Electrical Engineering, 92, 107104. https://doi.org/10.1016/j.compeleceng.2021.107104
- Moysiadis, V., Sarigiannidis, P., Vitsas, V., & Khelifi, A. (2021). Smart Farming in Europe. Computer Science Review, 39, 100345. https://doi.org/10.1016/j.cosrev.2020.100345
- Navarro, E., Costa, N., & Pereira, A. (2020). A Systematic Review of IoT Solutions for Smart Farming. Sensors, 20(15), 4231. https://doi.org/10.3390/s20154231
- Said Mohamed, E., Belal, A., Kotb Abd-Elmabod, S., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971–981. https://doi.org/10.1016/j.ejrs.2021.08.007
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