Background
For this project, autonomous technology, or artificial intelligence (AI), is described as “systems in which the designer has not predetermined the responses to every condition. Such systems are capable of surprising their designers and behaving in unexpected ways” (Omohundro 2014). This technology is attractive, especially in militaristic and economic contexts, because of the ability to make informed, split-second decisions (Omohundro 2014). Because of this, AI is gaining huge popularity around the world, and many view it as an important step in human technological development (Omohundro 2014). However, AI has plenty of other implications in almost any environment, including agriculture.
The reasons for implementing AI in agriculture are largely to provide a smart way to monitor crops and provide just the right amount of resources for the specific crop conditions (Dengel 2013). This would greatly help increasing resource efficiency in large scale agriculture while also reducing labor costs. By creating a more efficient system, farms could produce more food and biofuel and account for changing popular tastes (Dengel 2013). It would allow for farms to more easily adapt to a changing climate as well (Dengel 2013). Some specific technologies that are currently being explored include autonomized weed control systems that would replace the current major method of weed control by hand (Slaughter 2008). Other exciting developments are occurring in precision agriculture sensors that take data from soil and GPS to control, for example, irrigation systems (Dong 2013).
However, there are also drawbacks to using AI technology in agriculture. Common to most AI applications, there is concern over laborers losing work as robotics and sensors take over jobs (Omohundro 2014). Also a common concern for AI are the unintended consequences of having a self-learning intelligence (Omohundro 2014). A developer cannot predict everything that will happen once the AI is released, which could prove detrimental without some way to control the system. Other concerns include the current high prices of AI technology, yet these may go down with further research and development (Dengel 2013). Finally, there are also concerns of pollution and technological waste resulting from these technologies that could leak toxins into the soil and surrounding environment (Nesheim et al. 2015).
Research Question
To what extent can adapting current autonomous technologies in agricultural systems lead to more efficient agricultural production, and are the current drawbacks too great for wide scale implementation?
Methods
- Literature review of autonomous technology in agriculture
- This will provide an overview of the current state of AI in agriculture and address whether or not the time is right for wide scale implementation or if more research and development is required
- Includes a comprehensive look into research papers, patents, and companies started on the subject
- This will focus on applications primarily in industrial United States agriculture, as new technologies are more likely to be adopted here before anywhere else
- Applications in other contexts will be considered as well but not the focus
- Interviews with various key actors: agricultural AI developers, early adopter farmers, opponents to AI use
- This will complement my literature review by providing a deeper look into how those most invested in AI view its use in agriculture
References
Dengel, Andreas. 2013. “Special Issue on Artificial Intelligence in Agriculture.” KI – Künstliche Intelligenz 27 (4): 309–11. doi:10.1007/s13218-013-0275-y.
Dong, Xin, Mehmet C. Vuran, and Suat Irmak. 2013. “Autonomous Precision Agriculture through Integration of Wireless Underground Sensor Networks with Center Pivot Irrigation Systems.” Ad Hoc Networks, Theory, Algorithms and Applications of Wireless Networked RoboticsRecent Advances in Vehicular Communications and Networking, 11 (7): 1975–87. doi:10.1016/j.adhoc.2012.06.012.
Omohundro, Steve. 2014. “Autonomous Technology and the Greater Human Good.” Journal of Experimental & Theoretical Artificial Intelligence 26 (3): 303–15. doi:10.1080/0952813X.2014.895111.
Nesheim, Malden C., Maria Oria, Peggy Tsai Yih, Environmental Committee on a Framework for Assessing the Health, Food and Nutrition Board, Board on Agriculture and Natural Resources, Institute of Medicine, and National Research Council. 2015. “Overview of the U.S. Food System.” In A Framework for Assessing Effects of the Food System. The National Academic Press.
Slaughter, D. C., D. K. Giles, and D. Downey. 2008. “Autonomous Robotic Weed Control Systems: A Review.” Computers and Electronics in Agriculture, Emerging Technologies For Real-time and Integrated Agriculture Decisions, 61 (1): 63–78. doi:10.1016/j.compag.2007.05.008.