Getting value from artificial intelligence in agriculture
Matthew J. Smith
+ Author Affiliations
- Author Affiliations
Microsoft Digital, Microsoft Corporation, 2 Kingdom Street, London, W2 6BD, UK. Email: matthew.smith@microsoft.com
Animal Production Science 60(1) 46-54 https://doi.org/10.1071/AN18522
Submitted: 27 August 2018 Accepted: 30 October 2018 Published: 21 November 2018
Abstract
Artificial intelligence (AI) is beginning to live up to its promise of delivering real value, driven by recent advances in the availability of relevant data, computation and algorithms. In the present paper, I discuss the value to agriculture from AI over the next decade. The more immediate applications will be to improve precision information about what is happening on the farm by improving what is being detected and measured. A consequence of this are more accurate alerts to farmers. Another is an increased ability to understand why phenomena occur in farm systems, so as to improve their management. From improved data and understanding come improved predictions, enabling more optimal decisions about how to manage farm systems and stimulating the development of decision support and recommender systems. In many cases, robotics and automated systems will remove much of the need for human decision-making and improve farm efficiencies and farm health. Artificial intelligence will also be needed to enable organisations to harness the value of information distributed throughout supply chains, including farm data. Digital twins will also emerge as an important paradigm to improve how information about farm entities is organised to support decision-making. There are also likely to be negative impacts from AI, such as disruption to the roles and skills needed from farm workers, indicating the need to consider the social and ethical impacts of AI each time a new capability is introduced. I conclude that understanding these challenges more deeply tends to highlight new opportunities for positive change.
Additional keywords: digital twin, precision, robotics, supply chain, traceability.
References
Anderson WK (2010) Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research 116, 14–22.| Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar.Crossref | GoogleScholarGoogle Scholar |
Banerjee A, Bandyopadhyay T, Acharya P (2013) Data analytics: hyped up aspirations or true potential? Vikalpa 38, 1–12.
| Data analytics: hyped up aspirations or true potential?Crossref | GoogleScholarGoogle Scholar |
Belayneh A, Adamowski J (2018) Drought forecasting: artificial intelligence methods. In ‘Exploring natural hazards’. (Eds D Bartlett, R Singh) pp. 207–224. (Chapman and Hall/CRC: New York)
Berckmans D (2008) Precision livestock farming (PLF). Computers and Electronics in Agriculture 62, 1
| Precision livestock farming (PLF).Crossref | GoogleScholarGoogle Scholar |
Box GEP (1976) Science and statistics. Journal of the American Statistical Association 71, 791–799.
| Science and statistics.Crossref | GoogleScholarGoogle Scholar |
Buchanan BG (2005) A (very) brief history of artificial intelligence. AI Magazine 26, 53–60.
Burgess A (2017) ‘The executive guide to artificial intelligence.’ (Springer International Publishing)
Butler D, Holloway L, Bear C (2012) The impact of technological change in dairy farming: robotic milking systems and the changing role of the stockperson. Journal of the Royal Agricultural Society of England 173, 1–6.
Caldararu S, Purves DW, Smith MJ (2017) The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1. 0). Geoscientific Model Development 10, 1679–1701.
| The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1. 0).Crossref | GoogleScholarGoogle Scholar |
Bughin J, Hazan E, Ramaswamy S, Chui M, Allas T, Dahlström P, Henke N, Trench M (2017) ‘Artificial intelligence-the next digital frontier.’ Online discussion paper. (McKinsey and Company Global Institute) Available at https://www.mckinsey.com/~/media/mckinsey/industries/advanced%20electronics/our%20insights/how%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/mgi-artificial-intelligence-discussion-paper.ashx [Verified 13 November 2018]
Driessen C, Heutinck LF (2015) Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms. Agriculture and Human Values 32, 3–20.
| Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms.Crossref | GoogleScholarGoogle Scholar |
Duckett T, Pearson S, Blackmore S, Grieve B, Smith M (2018) White paper – agricultural robotics: the future of robotic agriculture. In ‘EPRSC, 2018 international robotics showcase’. Available at http://eprints.uwe.ac.uk/36839 [Verified 13 November 2018]
Eastwood CR, Chapman DF, Paine MS (2012) Networks of practice for co-construction of agricultural decision support systems: case studies of precision dairy farms in Australia. Agricultural Systems 108, 10–18.
| Networks of practice for co-construction of agricultural decision support systems: case studies of precision dairy farms in Australia.Crossref | GoogleScholarGoogle Scholar |
Eastwood CR, Klerkx L, Nettle R (2017a) Dynamics and distribution of public and private research and extension roles for technological innovation and diffusion: case studies of the implementation and adaptation of precision farming technologies. Journal of Rural Studies 49, 1–12.
| Dynamics and distribution of public and private research and extension roles for technological innovation and diffusion: case studies of the implementation and adaptation of precision farming technologies.Crossref | GoogleScholarGoogle Scholar |
Eastwood CR, Dela Rue BT, Gray DI (2017b) Using a ‘network of practice’ approach to match grazing decision-support system design with farmer practice. Animal Production Science 57, 1536–1542.
| Using a ‘network of practice’ approach to match grazing decision-support system design with farmer practice.Crossref | GoogleScholarGoogle Scholar |
Eastwood CR, Ayre M, Dela Rue B (2018) Farm advisors need to adapt to provide value to farmers in a smart farming future. In ‘Farming systems: facing uncertainties and enhancing opportunities, 13th European IFSA Symposium’, 1–5 July 2018, Chania, Crete, Greece.
Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics 12, 411–437.
| Survey of computational intelligence as basis to big flood management: challenges, research directions and future work.Crossref | GoogleScholarGoogle Scholar |
Galon N (2010) The use of pedometry for estrus detection in dairy cows in Israel. The Journal of Reproduction and Development 56, S48–S52.
| The use of pedometry for estrus detection in dairy cows in Israel.Crossref | GoogleScholarGoogle Scholar |
Haag S, Anderl R (2018) Digital twin: proof of concept. Manufacturing Letters 15, 64–66.
| Digital twin: proof of concept.Crossref | GoogleScholarGoogle Scholar |
Hassan H, Aue A, Chen C, Chowdhary V, Clark J, Federmann C, Huang X, Junczys-Dowmunt M, Lewis W, Li M, Liu S (2018) Achieving human parity on automatic Chinese to English news translation. Available at https://arxiv.org/abs/1803.05567 [Verified 13 November 2018]
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In ‘Proceedings of the IEEE conference on computer vision and pattern recognition’. pp. 770–778. 26 June–1 July 2016, Las Vegas
Holloway L (2007) Subjecting cows to robots: farming technologies and the making of animal subjects. Environment and Planning. D, Society & Space 25, 1041–1060.
| Subjecting cows to robots: farming technologies and the making of animal subjects.Crossref | GoogleScholarGoogle Scholar |
Jago Jago Jago Jago (2013) Precision dairy farming in Australasia: adoption, risks and opportunities. Animal Production Science 53, 907–916.
Kannagi L, Ramya C, Shreya R, Sowmiya R (2018) Virtual conversational assistant: ‘The FARMBOT’. International Journal of Engineering Technology Science and Research 5, 520–527.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521, 436
Morgan RPC, Nearing M (2016) ‘Handbook of erosion modelling.’ (John Wiley & Sons, Chichester)
Mostaço GM, De Souza ÍRC, Campos LB, Cugnasca CE (2018) AgronomoBot: a smart answering Chatbot applied to agricultural sensor networks. In ‘14th international conference on precision agriculture’, 24 June–27 July 2018, Montreal, Quebec, Canada. pp. 1–13.
Mueller JP, Massaron L (2018) ‘Artificial intelligence for dummies.’ (John Wiley & Sons: Hoboken, NJ)
Nabavi-Pelesaraei A, Rafiee S, Mohtasebi SS, Hosseinzadeh-Bandbafha H, Chau K-w (2018) Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. The Science of the Total Environment 631–632, 1279–1294.
| Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.Crossref | GoogleScholarGoogle Scholar |
Nuthall PL (2012) The intuitive world of farmers: the case of grazing management systems and experts. Agricultural Systems 107, 65–73.
| The intuitive world of farmers: the case of grazing management systems and experts.Crossref | GoogleScholarGoogle Scholar |
Nuthall PL, Old KM (2018) Intuition, the farmers’ primary decision process. A review and analysis. Journal of Rural Studies 58, 28–38.
| Intuition, the farmers’ primary decision process. A review and analysis.Crossref | GoogleScholarGoogle Scholar |
Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Computers and Electronics in Agriculture 153, 69–81.
| Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review.Crossref | GoogleScholarGoogle Scholar |
Retzbach A, Maier M (2015) Communicating scientific uncertainty: media effects on public engagement with science. Communication Research 42, 429–456.
| Communicating scientific uncertainty: media effects on public engagement with science.Crossref | GoogleScholarGoogle Scholar |
Russell SJ, Norvig P (2016) ‘Artificial intelligence: a modern approach.’ (Pearson Education Ltd: Harlow, England)
Rutten CJ, Velthuis AGJ, Steeneveld W, Hogeveen H (2013) Invited review: sensors to support health management on dairy farms. Journal of Dairy Science 96, 1928–1952.
| Invited review: sensors to support health management on dairy farms.Crossref | GoogleScholarGoogle Scholar |
Schroeder TC, Tonsor GT (2012) International cattle ID and traceability: competitive implications for the US. Food Policy 37, 31–40.
| International cattle ID and traceability: competitive implications for the US.Crossref | GoogleScholarGoogle Scholar |
Settles B (2012) Active learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 6, 1–114.
| Active learning.Crossref | GoogleScholarGoogle Scholar |
Sirosh J (2018) Planet-scale land cover classification with FPGAs. In ‘Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining’, 19–23 August. pp. 2877–2877. (ACM: London)
Smith B, Shum H (2018) ‘The future computed: artificial Intelligence and its role in society.’ (Microsoft Corporation: Redmond, WA)
Sreelatha M, Nasira GM, Thangamani P (2016) Patten recognition for toxic gases based on electronic nose using artificial neural networks. In ‘3rd international conference on computing for sustainable global development (INDIACom)’, 16–18 March, New Dehli, India. pp. 3075–3079. (IEEE)
Stilgoe J, Owen R, Macnaghten P (2013) Developing a framework for responsible innovation. Research Policy 42, 1568–1580.
| Developing a framework for responsible innovation.Crossref | GoogleScholarGoogle Scholar |
Syfert MM, Smith MJ, Coomes DA (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS One 8, e55158
| The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.Crossref | GoogleScholarGoogle Scholar |
Vasisht D, Kapetanovic Z, Won J, Jin X, Chandra R, Sinha SN, Kapoor A, Sudarshan M, Stratman S (2017) FarmBeats: an IoT platform for data-driven agriculture. In ‘Proceedings of the 14th USENIX Symposium on Networked Systems (NSDI ’17)’, 27–29 March 2017, Boston, MA. pp. 515–529.
Vieira GC, de Mendonça AR, da Silva GF, Zanetti SS, da Silva MM, dos Santos AR (2018) Prognoses of diameter and height of trees of eucalyptus using artificial intelligence. The Science of the Total Environment 619–620, 1473–1481.
| Prognoses of diameter and height of trees of eucalyptus using artificial intelligence.Crossref | GoogleScholarGoogle Scholar |
Wang W, Yang N, Wei F, Chang B, Zhou M (2017) Gated self-matching networks for reading comprehension and question answering. In ‘Proceedings of the 55th annual meeting of the Association for Computational Linguistics (Vol. 1: long papers)’, 30 July–4 August 2017, Vancouver, Canada. pp. 189–198. Available at https://doi.org/10.18653/v1/P17-1018 [Verified 13 November 2018]
Wathes CM, Kristensen HH, Aerts JM, Berckmans D (2008) Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall? Computers and Electronics in Agriculture 64, 2–10.
| Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall?Crossref | GoogleScholarGoogle Scholar |
Wolfert S, Ge L, Verdouw C, Bogaardt M-J (2017) Big data in smart farming: a review. Agricultural Systems 153, 69–80.
| Big data in smart farming: a review.Crossref | GoogleScholarGoogle Scholar |
Yan Y, Feng CC, Wan MPH, Chang KTT (2015) Multiple regression and artificial neural network for the prediction of crop pest risks. In ‘International conference on information systems for crisis response and management in Mediterranean countries’. pp. 73–84. (Eds NB Ben Saoud, C Adam, C Hanachi) (Springer: Cham, Switzerland)
