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Top AI Trends to be Leveraged by Manufacturing, Agriculture and Healthcare Industries in 2020

  • By  [x]cube LABS

  • Published: Jun 18 2020

  • AI vs Coronavirus: Some AI assistance

    AI tools and services are being used or offered by companies around the world to help fight the coronavirus pandemic.

    China’s Tianhe-1 supercomputer is offering doctors around the world free access to an AI diagnosis tool for identifying coronavirus patients based on a chest scan. The supercomputer can sift through hundreds of images generated by computed tomography (CT) and offer diagnosis in about 10 seconds.

    AI powered computational platforms are being used by researchers and institutions working towards a vaccine to accelerate data transfer and computation time in areas such as virtual drug screening.

    AI solutions are being used to estimate the trajectory of a coronavirus outbreak in a specific region using a machine learning algorithm based on public data gathered from 31 provinces in China. Within China, it has a 98 percent accuracy rate.

    AI systems are being used to examine large repositories of medical information to identify compounds that effectively block a cellular pathway that appears to allow the virus into cells to make more virus particles. This can help researchers identify approved drugs that might block the viral replication of COVID-19.

    While the outlook around the world is currently grim, some of these AI-powered tools and developments offer a glimmer of hope that we may be able to reduce the virus’s spread, improve treatment for patients, and ultimately conquer the coronavirus sooner than otherwise would have been possible.


    AI in the manufacturing market is expected to grow at a CAGR of 39.7% from 2019 to 2027 to reach $27 billion by 2020 according to a recent report by Meticulous Market Research. The manufacturing industry has always been open to adopting new technologies. Drones and industrial robots have been a part of the manufacturing industry since the 1960s. With the adoption of AI if companies can keep inventories lean and reduce the cost, there is a high likelihood that the American manufacturing industry will experience an encouraging growth. Having said that, the manufacturing sector has to gear up for networked factories where supply chains, design teams, production lines, and quality control are highly integrated into an intelligent engine that provides actionable insights.

    Manufacturing businesses in the US using AI are performing 11.5 percent better than those that aren’t according to a research by Microsoft. AI benefits the industry. Why? Its possible applications are extensivend the stats are enticing. According to McKinsey, 50 percent of companies that invest in AI over the next five to seven years will have the potential to double their cash flow; manufacturing is leading the way due to its heavy reliance on data.

    Here’s how the manufacturing industry is leveraging AI today:

    1. Predictive Maintenance

      Maintenance is a key area that can drive major cost savings and production value around the world. The cost of machine downtime is high: according to the International Society of Automation, $647 billion is lost globally each year.

      With AI and machine learning, we have the ability to process massive amounts of sensor data faster than ever before. This gives companies an unprecedented chance to improve upon existing maintenance operations.

      Predictive maintenance uses data from various sources like historical maintenance records, sensor data from machines, and weather data to determine when a machine will need to be serviced. Leveraging real-time asset data plus historical data, operators can make more informed decisions about when a machine will need a repair. Predictive maintenance takes massive amounts of data and through the use of AI and predictive maintenance software, translates that data into meaningful insights and data points.

    2. Next-generation software design

      It’s a new way of looking at things. Designers or engineers enter their ultimate design goal into generative design software, complete with cost constraints, preferred materials and methods. The software then takes the original idea and explores different solutions to make it a reality on the spot. The result: you get many design possibilities, a conclusion of whether they’ll work or not, and a recommendation for the best solution. It could be used from everything from aircraft wing design to plastic molds for a phone case.

      Brian Matthews, Vice President of Platform Engineering at Autodesk, US, says it could achieve 50,000 days of engineering in one day.

    3. Industrial automation

      For the manufacturing industries to stay competitive with other domains there is a serious need for them to come out of their legacy systems by automating their traditional processes. Every manufacturer needs to have the potential of implementing machine learning applications in order to achieve predictive accuracy in production.

      The growing changes in consumer behavior in regards to customization and product quality, it is difficult for the manufacturers to make changes in the production system (like re-programming or re-tooling the application) in a short time. This is where machine learning benefits the manufacturers. The machine learning application examines and performs maintenance on production apparatus, optimizes the production & supply chain efficiency by reprogramming the unit computers and assists in delivering products on-time.

    4. Reduced operational costs

      Many companies are viewing the introduction of AI into the manufacturing industry with trepidation, as it requires a huge capital investment. On the other hand, the ROI is significant and increases as time goes on. Once intelligent machines begin to take over the daily activities of a factory floor, businesses will benefit through considerably reduced operating costs, with predictive maintenance helping additionally to reduce machine downtime.

      These days, consumers are increasing their demand for unique, personalised or customised products, while continuing to expect the best value. Integrating machine learning and CAD means that systems can be designed and tested in a virtual model before they are put into production, thus reducing the cost of trial-and-error machine testing.

    5. Novel opportunities for humans

      As AI takes over the manufacturing plant and automates simple and ordinary human tasks, workers will get to focus on complex and innovative tasks. While AI takes care of unskilled labor, humans can focus on driving innovation and routing business to advanced levels.


    The overall AI in agriculture market is projected to grow from an estimated USD 1 billion in 2020 to USD 4 billion by 2026, at a CAGR of 25.5% between 2020 and 2026 as per the latest report by MarketsandMarkets.

    Americas accounted for the largest share of AI in the agriculture market in 2019. In the Americas, large scale agriculture players are already using AI technology to significantly improve the speed and accuracy of their planting and crop management techniques.

    AI offers very innovative use cases that in turn help farmers prevent losses and maximize their yield.  AI technologies such as machine learning, predictive analytics and computer vision have gained tremendous popularity for agriculture applications such as the following:

    1. Crop and soil health 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. Farmers and farm enterprises can monitor and identify possible defects and nutrient deficiencies in soil using AI.

      With image recognition and deep learning applications, flora patterns in agriculture can be analysed.  Such AI-enabled applications are supportive in understanding soil defects, plant pests, and diseases.

    2. Decrease pesticide usage

      Weeds compete with neighboring crops for light, water and nutrients, costing the farming industry billions each year in agricultural yield. To keep a better eye on fields, improve crop yields and reduce the use of pesticides, farmers and agriculture researchers are turning to AI.

      Farmers can use AI to manage weeds by implementing computer vision, robotics, and machine learning. With the help of  AI, data is gathered to keep a check on weeds which helps farmers to spray chemicals only where the weeds are. This directly reduced the usage of the chemical spraying an entire field. As a result, AI reduces herbicide usage.

    3. Agriculture bots

      AI-enabled agriculture bots help farmers to find more efficient ways to protect their crops from weeds. This is also helping to overcome the labor challenge. AI bots in the agriculture field can harvest crops at a higher volume and faster pace than human laborers. By leveraging computer vision helps to monitor the weed and spray them.

      The spreading of weeds is estimated to cause a loss of approximately $43 billion dollars worth of crops a year. For these reasons, farmers are looking for effective ways to combat weeds.

    4. Forecast weather data

      Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes. AI in an advanced way is helping  farmers to remain updated with  data related to weather forecasting.

      AI is helping farmers analyse the gathered data and learn more from it in turn enabling smarter decision making. The forecasted/ predicted data helps farmers increase yields and profits without risking the crop.

    5. Precision farming

      In this process, farmers can detect pests, diseases in plants, and poor plant nutrition of farms with the help of AI. Also, AI sensors can identify and target weeds and then decide which weed killers or herbicides to apply within the right zone. It assists to thwart over application of herbicides and excessive toxins that find their way in today’s daily food.

      By leveraging AI, farmers are also creating seasonal forecasting models to enhance agricultural accuracy, harvest quality and productivity.


    AI is revolutionizing the way companies access and process data to become smarter and more efficient organizations. With the help of AI we are able to achieve superhuman results that would have rather been impossible, saving a lot of time and resources and eliminating several overheads. Business leaders who recognize the value of AI and start implementing it into their businesses are definitely bound to benefit from it in the years to come.

    So are you going to be one of them?

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