AI can fill labour shortages, provide smarter agricultural management to solve food security and save lives

AI can fill labour shortages, provide smarter agricultural management to solve food security and save lives

AI is projected to revolutionize agriculture. AgTech, one of the most under-developed areas of AI will, in about one to two years, become one of the most important areas of AI, on par with cybersecurity. Agricultural automation, for example, is critical to sustain food production in the face of massive global food security issues, starvation in Africa and an agricultural labour crisis caused by aging farmers and an aging population.

Agriculture is a US$5 trillion industry, representing 10% of global consumer spending. Investment in agricultural technology startups was US$3.23 billion in 2016. While it is uncertain how much of that investment was in AI specifically, analysts have determined that US$363 million was invested in farm management software, sensors and the IoT – areas with AI overlap. It is estimated that by 2020, over 75 million IoT devices for agriculture will be in use, and by 2050, the average farm will generate 4.1 million data points per day, up from 190,000 in 2014.

The agribot market is expected to grow to US$16.3 billion by 2020, up from US$817 million in 2013 and encompass growth in adaptive robots, autonomous navigation in fields, automated farm operations, computer vision and precision agriculture. Drones for farming, for example, is expected to be at US$2 billion, and some expect that 80% of the commercial market for drones will be dedicated to agriculture, adding approximately US$60 billion in economic activity.

Precision agriculture, which uses tech and big data analytics to optimize agricultural inputs for predictive AI, will be a US$4.55 billion market by 2020. Emerging markets are expected to see the fastest growth in precision AI for agriculture.

AI can facilitate large scale farming production, processing, storage and distribution of agricultural goods more efficiently than humans and moreover, can be used to collect data about crops and be programmed to apply needed inputs such as water, chemicals or fertilizers when and where needed, and fill gaps in the agricultural labour force.

The range of AgTech development is impressive. Tech firms are working on satellites that monitor drought patterns, tractors that can detect and kill sick plants and smartphone apps that can identify what disease destroyed a crop to allow for safer, better controlled and more tailored pesticide use. In the US, scientists developed an AI program with machine learning that can correctly identify crop diseases in 99.35% of the cases based on a catalogue of images from which it learns.

In the US, a company is developing a self-driving tractor and another, farmbots that “learn” as they roam across farmland, using sensors to identify pests, diseases and weeds. The farmbots feed data about the crops in real time to computers, which analyze the data and recommend farming decisions based on its findings.

Blue River Technology, a US company, developed a tractor with machine learning capabilities. It can photograph 5,000 plants a minute while driving on farm fields and can identify each sprout as lettuce or a weed, allowing farmers to make tailored agriculture decisions. As well, some farmers use AI for pest control. PestID, a mobile app created by a pest control company, can cross-analyze pictures of bugs or vermin taken by a farmer in the field to identify the pest and provide information on what chemicals to use for pest control.

In Australia, a startup named The Yield develops microclimate crop and aquaculture sensing systems that use sensors, models, AI and apps to help farmers make decisions based on data collected from fields or waterways. Using predictive AI, it can predict when best to harvest, plant, feed and protect crops based on data points collected.

The Queensland University of Technology is building robots that will be able to perform manual agricultural tasks in orchards, such as fruit-picking, much faster than humans, improving agricultural output and use of resources. Abundant Robotics, a robotics and automation startup, is working to perfect and commercialize an apple-picking robot to reduce the $200 billion orchard farming industry’s reliance on seasonal labour – labour that hasn’t improved productivity in 20 years despite yield increases.

Entrepreneurs, especially in the US, are focusing on more efficient food production and processing, as well as optimization of crop growth using AI in creative ways. Cainthus, a machine vision company based in San Francisco and Ottawa uses facial recognition technology for cows that can identify cows by their facial features in 6 seconds, allowing farmers to monitor them more quickly and with minimal human interaction.

FarmView, an initiative at Carnegie Mellon University, is combining sensors, robotics and AI to create mobile field robots to improve plant breeding and crop-management practices. FarmView’s robots will be able to gather data from fields to help farmers make management decisions and allow them to grow more abundant and higher quality food with fewer resources. Carnegie Mellon is also developing AI for plant breeding, wherein robotically gathered plant phenotype data is combined through machine learning with genetic and environmental data to help accelerate the breeding process by evaluating plants more efficiently, selecting desirable traits, such as yield or resistance to disease.

Combining farming with retail, New Jersey-based Bowery launched a vertical farming business that uses automation, machine learning and vision systems to monitor and tend to crops to provide a scalable way to provide food on the retail level. The AI systems draw in data from a network of sensors across the facility that measure a variety of data points impacting growth of plants using cameras and computer vision to detect changes in plants. By correlating these images against other variables detected by the sensors, such as humidity or temperature, the software can determine and adjust the drivers of changes in plant health, taste or quality.

Farmnote, a Japanese startup, developed a wearable that collects data from cows for all sorts of farming purposes that is used to control and monitor livestock. Farmnote’s wearable device is attached around the cow’s neck like a watch. It gathers information about a cow’s activity in real time and texts notices to the mobile phones of farmers to alert them to material changes, such as when cows are unwell, roaming, ovulating or giving birth.

AI, finance and AgTech is a growing intersection of potential growth. FarmDrive, a Kenyan data analytics startup, helps small farmers in Africa access credit from local banks by generating credit scores by using data inputs from farmers and farm data derived from satellite, agronomic and local economic datasets using algorithms. It is also developing decision-making tools for financial institutions to use to create financial products suitable for small farmers, such as tailoring loan paybacks to match harvest yields.

Agricultural equipment giant John Deere funds an AgTech accelerator to support entrepreneurs who have an idea, intellectual property or a prototype for agricultural innovation with seed funding, as well as mentoring with strategic companies partnering with the program.

The most important application of AgTech is food security. With a global population expected to reach 9 billion by 2050, agricultural production must rise by 70% to meet human food needs. Food security could be achieved with investments in AI with innovative ecosystems to improve yield, better manage food production, and to improve global food distribution logistics infrastructure to provide food security for the 108 million people suffering from crisis level food insecurity, exacerbated by protracted wars and record staple food prices, especially in Africa and the Middle East.

Canada is a case in point that has the potential for AI leadership to emerge in agriculture if government agencies provided grants to startups. In Ontario alone, the agri-food sector adds $36.4 billion to the province’s economy and supports 800,000 jobs, surpassing the financial services sector in economic importance. In Canada, the labour shortage in the agricultural sector will reach 113,800 jobs by 2025 due to a combination of factors, including an aging workforce and the rural location of farms. Even Canada has pockets of severe food security issues equal to Africa; for example, 48% of First Nations living on a reserve in Ontario experience severe food insecurity.

AI represents a key way to fill labour shortages and at the same time provide smarter agricultural management that can solve food security and save lives.

The Digital Finance Institute wrote and published a Report on Commercial AI (available here) that canvassed the pulse of AI from media stories and academic reports, covering various sectors of the economy. This article covers the agriculture portion of the Report.

[1] Christine Duhaime, The Promises of AI for Africa, Lagos, Nigeria, April 26, 2017.

[2] Madalina Irimia, IBM, “Five ways agriculture could benefit from artificial intelligence,” December 14, 2016.

[3] Louisa Burwood-Taylor, AgTech, A Year of Contrasts: Agtech Funding Dips to $3.2bn while Deal Activity Rises 10% in 2016,” January 31, 2017.

[4] Madalina Irimia, IBM, “Five ways agriculture could benefit from artificial intelligence,” December 14, 2016.

[5] Beijia Ma, Sarbjit Nahal and Felix Tran,Bank of America Merrill Lynch, “Thematic Investing,” December 16, 2015.

[6] Beijia Ma, Sarbjit Nahal and Felix Tran,Bank of America Merrill Lynch, “Thematic Investing,” December 16, 2015.

[7] H. Hagras, M. Colley, V. Callaghan, and M. Carr-West, Autonomous Robots, “Online learning and adaptation of autonomous mobile robots for sustainable agriculture,” 2002.

[8] Matt Simon, Wired, “The Future of Humanity’s Food Supply is in the Hands of AI,” May 25, 2016.

[9] Joseph Byrum, Farming Futures, “Artificial intelligence in agriculture: what to expect,” December 23, 2016.

[10] Matt Simon, Wired, “The Future of Humanity’s Food Supply is in the Hands of AI,” May 25, 2016.

[11] Clint Boulton, CIO, “AI, machine learning blossom in agriculture and pest control,” March 22, 2017.

[12] Sally Dakis, ABC News, “Global giants KPMG, Bosch invest in Australian-developed agricultural technology,” April 6, 2017.

[13] The Yield, Sensing+ Agri – Microclimate Sensing System, 2017.

[14] David Sparkes, The Guardian, “Mining, manufacturing and fruit picking: can automation save Mackay jobs?,” November 27, 2016.

[15] Louisa Burwood-Taylor, AgFunderNews, “Abundant Robotics Raises $10m Series A for Apple Picking Robot Led by GV,” May 3, 2017.

[16] Jon Mundy, Tech Radar, “How artificial intelligence could save humanity’s food supply,” September 27, 2016.

[17] Jennifer Kite-Powell, Forbes, “How Sensors, Robotics and Artificial Intelligence Will Transform Agriculture,” March 19, 2017.

[18] Louisa Burwood-Taylor, AgFunderNews, “Bowery Launches AI-Enabled Indoor Farming Business with $7.5m in Seed Funding,” February 23, 2017.

[19]Louisa Burwood-Taylor, AgFunderNews, “Japan’s Farmnote Raises $4.6m for IoT Device and Data Analytics in Livestock Farming,” May 11, 2017.

[20]Louisa Burwood-Taylor, AgFunderNews, “FarmDrive Raises Funding to Help Africa’s Smallholder Farmers Get Finance with Credit Scoring Algorithm,” February 16, 2017.

[21] Louisa Burwood-Taylor, AgFunderNews, “Why John Deere Invested in the Iowa Agritech Accelerator,” April 5, 2017.

[22] Pietro Aldobrandini, E-Agriculture, “Artificial intelligence in agriculture: How farming is going automated with robots,” May 17, 2016.

[23] Christine Duhaime, The Promises of AI for Africa, Lagos, Nigeria, April 26, 2017.

[24] Statistics Canada, “OMAFRA,” 2017.

[25] Chiefs of Ontario, “First Nations Regional Health Survey,” 2012.

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