Rob Trice :
(From previous issue)
According to Pitchbook, over the last decade, over $17b has been invested in AI related startups in the U.S. There have already been 200+ AI-related acquisitions since 2012. This activity has mostly been led by the big tech giants like Google, Facebook, Microsoft, and Amazon as they look to gain access to capabilities to help transform industries as diverse as transportation, healthcare, retail, and manufacturing.
While AI has become a mainstay of the tech community, many of the major ag input companies, equipment manufacturers, and service providers have yet to vigorously pursue AI applications in agriculture.
Part of this hesitancy may be the overall lack of familiarity with AI advances and the potential applications, which this article hopes to at least partially remedy.
Additionally, the development of AI algorithms can be challenging in an agricultural setting. AI applications require large amounts of data to properly train the algorithms. In agriculture, while there is a significant amount of spatial data, much of the data is only available once per year during the growing season. Thus, it can be years before a statistically significant temporal data set about a given field or farm is collected. Often, the data collected in the fields needs extensive pre-processing (cleaning up) before it can be reliably used as input to AI algorithms.
Today, there continue to be challenges associated with getting connected to the data. The Wall Street Journal recently wrote about how cell phone reception is spotty or nonexistent in farms, which makes it hard to transfer the data to a location where it can be analyzed.
A lack of standards, perceived poor transparency around data use and ownership, and the difficulty of gathering and sharing data has lead to a situation where AI algorithm developers in Ag are still starved for data. Luckily, products like the Climate Corporation’s FieldView Drive, John Deere’s JD Link, and Farmobile’s PUC are aiming to make the collection and transfer of equipment data easy and seamless.
Emerging agricultural technology (AgTech) companies developing AI algorithms may also be exacerbating the problem. Many startups are building decision automation tools while there still exist large gaps in data collection, preparation, and benchmarking capabilities. Farms have historically lacked the information technology infrastructure and data warehousing systems that Silicon Valley tech firms have relied on to develop and implement AI applications. The data infrastructure on the farm will need to become more robust before large scale agricultural AI deployment can be successful.
Further, some of these emerging companies have tended to avoid the use of scientifically validated, statistically controlled field trials to quantify the benefits of their products. Instead, these companies have used “lean” methods to get to market quickly with a small subset of customers, following the playbook for building a tech startup. While the lean method has worked well in software, in agriculture, a grower simply can’t risk adopting a new technology across their whole farm that may not work. Before launching a product, major agricultural companies put their products through years of field trials to ensure consistent performance and clear benefit. Even with this testing, many growers will want to see new products perform well on a subset of their own acres before complete adoption.
(To be continued)
