Successful people aren't above copy-catting. Farmers and others who design agriculture-specific technology have embraced artificial intelligence to the extent that its adoption is a bona fide industry trend. But how is AI being used, specifically? AI is a solution to many problems, but which inefficiencies are best suited for AI as a remedy?
These sort of sub-questions fall under a larger one: which AI uses in agriculture qualify as trends? These industry pros shared their insights — here's what they said:
1. Jeff Klaumann, Chief Technology Officer at Internet of Things America
“Machine learning-enabled solutions are the key trend driving adoption of AI in agriculture. IoT America recently announced the launch of the nation’s first IoT Soil Monitoring Managed Services. Because over 70% of the world’s drinkable water is used for agriculture, water resource management and conservation are important. For example, as part of IoT America’s service, producers can use soil moisture sensors as part of irrigation scheduling during the critical growing season and track improvements of water-holding capacity year-to-year during the winter. Use cases for managed soil vary across industries. We have seen needs in:
– Farm & Ranch applications
– Specialty farms and crops (orchards, berries, vineyards, etc.)
– Row crops, (corn, soybeans, etc.), and other grain farms (wheat, alfalfa, etc.)
– Turf/sod farms”
2. Ash Madgavkar, Founder of Ceres Imaging
“Computer vision and deep-learning algorithms are being leveraged by ag tech companies to process data captured from aerial imagery, allowing growers to monitor and manage crop and soil health. Over time, these AI/machine learning models develop to track and predict various environmental impacts like water stress, disease and pest infiltration. Growers can use the historical data to predict yield in season and analyze the ROI on various potential fixes or investments they could make to improve yield.”
3. Brad Constantinescu, President and CTO of Stone Soup Tech
“Given the recent advances in robotics, computer vision and reinforcement learning, many companies are now experimenting with agricultural robots (Agrobot, Abundant Robotics, FFRobotics, etc). There is a labor shortage in the farming industry, especially for unskilled seasonal workers. Robots are now being developed or used at a small scale for picking strawberries, apples or grapes, planting corn or picking weeds. As the tech advances, the interest grows and the prices drop, we can expect seeing robots on a wider scale in the next 5-10 years.”
4. Kirk Haney, CEO of Radicle Growth
“The application of vision systems to drive automated applications. Think about robotics such as Augean Robotics, a portfolio company, that is deploying an autonomous wheelbarrow to help with harvesting produce. An autonomous robot can follow a harvester and then autonomously bring the harvest to central location. This saves time, money and many qualitative benefits to the worker. Vision systems will continue to shape agriculture by way of low-earth orbit satellites, high speed drones and cameras mounted on ground vehicles.”
5. Ryan Phelan, partner at IP law firm Marshall, Gerstein & Borun
“Artificial Intelligence (AI) related patenting has exploded across all industries in the past several years, and AI-related inventions in the Ag space is no exception. This sentiment is echoed by the World Intellectual Property Organization (WIPO)’s 2019 report on Artificial Intelligence technology trends. For example, WIPO reports a 32 percent year-over-year growth in agriculture-related patent filings during the period from 2013 to 2016. However, this growth rate is dwarfed by other industry sectors, including software and telecommunications, where software, such as AI, is routinely applied. For this reason, the biggest untapped opportunities today for AI lies in industries, such as agriculture, that are outside the traditional software industry space.”
6. Roger Royse, founder of the Royse AgTech Innovation Network
“Big data and analytics is still the biggest area in which AI systems are used. Many companies are collecting massive amounts of data on agricultural areas, and strong deep learning systems are needed to spot trends. It is still a growing area. AI will also figure prominently with vertical, indoor or controlled environment ag. The indoor ag movement has made big advances this year and relies heavily on automated systems, which will require an AI component to scale.”
7. John McDonald, CEO at ClearObject
“Based on findings from the research firm, Emerj, the most popular applications of AI in agriculture appear to actually fall into three categories:
– Agricultural Robots – Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.
– Crop and Soil Monitoring – Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and/or software-based technology to monitor crop and soil health.
– Predictive Analytics – Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.”
8. Stephen Blum, CTO of PubNub
“Looking forward, it'll solve big challenges in the future, including agriculture performance monitoring and analysis. Agriculture businesses are creating more and more ways to collect data on the performance of farms and factories. From drone flyovers to low-powered sensors to smart machinery, these businesses are creating massive amounts of data. Today drones in the field can assist in lightweight observation and assessment data collection, and feed this data into AI/ML models to determine progress, quality and error detection.”
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