Robotics and Artificial Intelligence to Revolutionize the Future of Agriculture

Subscribe To Download This Insight

By Lian Jye Su | 4Q 2018 | IN-5242

Robotics and AI will not replace human in agriculture. On the contrary, both will augment farmers’ efficiency, increase yield, and reduce cost. This Executive Foresight offers insights into how robotics and AI are applied in agriculture practices and how this will impact the future of agriculture industry.

Registered users can unlock up to five pieces of premium content each month.

Log in or register to unlock this Insight.

 

A Differentiating Factor for AI and Robotics Investment in Europe and the United States 

NEWS


In the past year, the agriculture industry has witnessed an uptick in the amount of investments poured into robotics and AI solutions. Blue River Technology, a deep-learning startup focused on lettuce farming, grabbed headlines last year when it was acquired by John Deere for US$305 million. Other agricultural technology startups such as Bowery Farming, Abundant Robotics, Harvest CROO Robotics, and Prospera Technologies all receive multimillion-dollar funding in 2017. Following the rise of drone adoption, robotics and AI solutions are set to further increase the level of automation in the agriculture industry, helping large corporations and small plantation owners alike to address the challenges of rising labor costs, declining profit, reducing environmental impacts, and shifting to sustainable and precision farming practices.

Therefore, it is not surprising that most, if not all, of these companies are based in Western Europe or the United States. Given the constant struggle to find labor supply with aging demographics, western economies have been trying hard to introduce more automation into their farming practices. While Chinese startups make waves by drawing in large investment figures in sectors such as facial recognition, media and entertainment, conversational AI, and autonomous driving, Europe- and U.S.-based startups are currently in the lead to revolutionize one of the most ancient economies in the world.

Evolution of Robotics Technology in Agriculture

IMPACT


In the early 2000s, agriculture has been served by autonomous solution providers for agricultural machinery, such as tractors, harvesters, fertilizer sprayers, and soil cultivators. These solutions tend to be closed loop and implemented on the devices themselves. Heavy investment is required for calibration, configuration, and optimization. As each farm and plantation is unique in their environmental conditions and crop types, there are no economies of scale in the deployment of autonomous solutions, resulting in high deployment cost and equipment investment. Since then, the availability of cloud computing and large-scale data collection have further revolutionized the industry. Hardware platform agnostic autonomous software vendors, such as Autonomous Solutions, are helping to bring cost efficiency automation solutions to the agriculture industry.

As small unmanned aerial systems (UAS) become more accessible in the 2010s, the agriculture sector started to adopt small UAS’ for plantation imaging and surveying, agrochemical spraying and dispensation, and crop health analysis. Intel’s investment in PrecisionHawk in 2014 was a vote of confidence in the potential of aerial image data processing and analytics. However, all these advancements in robotics were hitherto restricted to large agricultural corporations.

With the aim to democratize robotics solutions in the agriculture industry, startups began to develop affordable robots with built-in autonomous solutions to perform specific tasks. Abundant Robotics, for example, offers apple-picking robots to apple plantations that have been facing regular labor shortages and shrinking margins. Small Robot Company, on the other hand, has three robots, Tom, Dick, and Harry, that perform soil monitoring, precision feeding and weeding, and precision drilling and planting, respectively. The startup even offers these robots to farmers and plantation companies as a service, removing financial risk and anxiety from their customers.

Data-driven AI

RECOMMENDATIONS


In ABI Research’s report on UAS for agriculture, it has been highlighted that all forms of hardware platforms are merely a platform. The key solution lies with data, or more precisely, the amalgamation of data from different sensory systems that contribute to the creation of AI models based on machine learning. In order to obtain better yield and reduce costs, large agriculture corporations have engaged cloud computing companies such as IBM and Accenture to assist them in their transformation to precision agriculture. Now, the proliferation of machine learning capabilities allowed startups to offer targeted solutions for agriculture use cases.

For example, Bowery Farming utilizes AI in delivering its vertical farming solution. Bowery OS, the startup’s proprietary software system, uses vision systems, automation technology, and machine learning to monitor plants and all the variables that drive its growth. Prospera Technologies, focuses on large-scale farming. The startup relies on advanced techniques in deep learning, computer vision, and data science to provide farmers with accurate remote agronomy and management solutions, which features in-field cameras and climatic sensors. Benson Hill Biosystems works with farmers at the biomolecular level, leveraging cloud computing, big data analytics, and plant biology to enable companies of all sizes to improve crop genetics.

Moving forward, ABI Research expects robotics and AI technologies to work hand-in-hand to create integrated farming solutions. The acquisition by John Deere is an indicator for future development, where large agriculture firms such as Cargill and Monsanto will acquire startups to boost their AI and robotics know-hows. At the moment, agriculture robotics platforms remain fragmented and rely on proprietary technologies, lacking severely in interoperability. Once cloud robotics starts to make its way into mainstream robotics solutions, the integration will become more seamless, as robotics hardware can reap the benefits of machine learning in AI platforms and provide tailored and targeted solutions for specific use cases.