Presentation of AI projects

Subject

Increase the incomes of small-scale food producers.

Technological module

Database / IA.

  1. Exploratory research

    1. Foodshed

      Foodshed

      1. Project carriers

        Dr. Dan Beckmann is the founder of Foodshed.io. He is an Obama ‘08 alum, a Peabody + Emmy Award winning Journalist, and has been featured on ABC News and Current TV. Through his work, he has built a nationwide network of over 3,000 creatives.

      2. Beneficiaries

        Foodshed.io is a mobile marketing app and logistics platform that connects small and medium-sized local producers to chefs, supermarkets and institutional buyers. Foodshed.io uses route optimization and real-time updates to help farmers, buyers and consumers benefit from a more localized economy.

      3. Users

        Farmers and buyers.

      4. Need

        They connect grocers, restaurants and institutions to local producers so that they can safely and economically source & market local, healthy, sustainably-produced food, at scale.

      5. Principle

        For farmers, it is a simple platform for accessing bigger markets, and responding to local demand and opportunities. Under the same platform, Foodshed.io streamlines and aggregates inventory from multiple farms, creating an efficient, reliable and transparent market where buyers can place all of their orders.

      6. Main technologies involved

        Database and API.

      7. Sources

        https://www.foodshed.io/

      8. Other info

        Consumer demand for local produce has never been greater, yet retailers struggle to meet it. Foodshed.io solves the problems faced by both buyers and sellers.

    2. Intello Labs

      Intello Labs

      1. Project carriers

        Milan Sharma is the CEO of Intello Labs. He is a visionary, entrepreneur, and innovator blended in one. Before he decided to take a bite out of food loss and waste, he dealt with analytics for the likes of Snapdeal and dunnhumby.

      2. Beneficiaries

        Growers/shippers/packers, wholesalers/traders,retailers and processors/food services.

      3. Users

        Growers/shippers/packers, wholesalers/traders,retailers and processors/food services.

      4. Need

        They are dedicated to digitizing food quality to build a world with no food loss and waste.

      5. Principle

        They tap the power of AI, ML, and computer vision to solve one of the biggest problems our world faces – cutting down food loss. They do so by digitizing the quality assessment of fresh fruits and vegetables. Their technology transforms quality processes, making them objective, efficient, and less wasteful.

      6. Main technologies involved

        AI, Machine Learning and computer vision.

      7. Sources

        https://www.intellolabs.com/

    3. Intelinair

      Intelinair

      1. Project carriers

        Al is co-founder and CEO of IntelinAir, Inc.as well as a multiple-exit serial entrepreneur; passionate technologist & technology investor; frequent speaker; avid reader & futurist; and a committed servant-leader. Al is a committed eco-preneur and believes in leveraging data to solve massive human problems, such as food security for the coming billions of humans.

      2. Beneficiaries

        Farmers

      3. Users

        Farmers

      4. Need

        Intelinair exists to help growers and agronomists improve crop performance.

      5. Principle

        They deliver actionable and detailed crop intelligence that can be used immediately. They provide insights, not just data, that result in increased yield and reduced cost all while saving growers time. In short, they create efficiencies that result in improved profit.

      6. Main technologies involved

        AI, machine learning, and computer vision.

      7. Sources

        https://www.intelinair.com/

      8. Other info

        Their approach to problem solving is more than just delivering data. They bring insight in a way that can be leveraged quickly and efficiently. Their unique “one touch to value” approach saves time while answering critical questions. Additionally, they deliver analysis from before planting until harvest is complete. Analysis, machine learning, and computer vision are their expertise and the benefit is insight delivered in a way customers can use it.

    4. Stellapps

      Stellapps

      1. Project carriers

        An IIT-Madras incubated company founded by a group of IITians and technologists with a strong industry background with over 18 years of Industry experience across Wipro, Nortel, Ericsson, Alcatel-Lucent, AT&T, Vodafone, Telstra, Bharti-Airtel, Aircel, Avaya, Cisco et al.

      2. Beneficiaries

        Farmers and cooperatives.

      3. Users

        Farmers and cooperatives.

      4. Need

        They digitize & optimise Milk Production, Milk Procurement & Coldchain Management through SmartMoo™ platform (Full Stack IoT solution) which helps dairy farmers and cooperatives maximize profits while minimizing effort.

      5. Principle

        Stellapps’ SmartMoo™ IoT platform acquires data via sensors that are embedded in milking systems, animal wearables, milk chilling equipment, and milk procurement peripherals. The data acquired is transmitted to the Stellapps SmartMoo™ Big Data Cloud Service Delivery Platform (SDP) where the Stellapps SmartMoo™ suite of applications analyze and crunch the received data before forwarding the analytics & data science outcome to various stakeholders over low-end and smart mobile devices.

      6. Main technologies involved

        Database, IoT and machine learning.

      7. Sources

        https://www.stellapps.com/

      8. Other info

        Stellapps mooON solution has two components : mooOn device & mooOn app. mooOn device is a pedometer for cattles which detects heat and various disorders based on their activities and their resting behaviour. mooOn app is a herd management application which gives recommendations to optimize herd performance.

  2. Deepening

    1. Intelinair

      Intelinair

      1. Carriers and actors of the project

        Intelinair Partner: DIGS Associates is a highly specialized consultancy providing actionable information and drainage water management plans for institutional and individual agricultural clients seeking capital improvements to their properties.

      2. Research question

        How does the AGMRI system calculate the upcoming storms and deliver alerts to the farmers?

      3. The reason you selected this project

        On their website, Intelunair claimed to strip out cost, improve yield and deliver increased profit. They see a future where digitization will have a positive impact throughout agriculture and will extend to 100+ million acres of crops. Their AGMRI system seems to be promising and well developed using AI technologies like Machine Learning, Computer Vision and Hi-res imagining.

        AGMRI system

    2. User scenario

      1. Users

        Farmers and cooperatives.

      2. Persona

        First name: Lucy
        Age: 28 years old.
        Activity / Profession: Corn farmer.
        Place of residence: In the countryside of the State of Illinois.
        Family status: Lives with her parents who are both corn farmers.
        Income: Corn sales.
        List of hobbies and passions: Reading and Internet surfing.
        List of needs Increase income, expand fileds.
        List of problems and frustrations: Unpredictable weather and lack of time to manage fields.
        Major issue related to the subject: How to use high technologies to deal with her problems.

      3. Key features

        • Manage fields
        • Get to know unpredictable situation
        • Detect and solve current problems of the fields
        • Observe the fields in distance
        • Identify equipment malfunctions
        • Identify drought-stress or overwater situation for her crops

      4. UX storyboard

        Storyboard

    3. Technical analysis

      1. General principle

        AGMRI, a cognitive AI platform purpose-built for agriculture Using Machine Learning, Computer Vision, and Hi-res imaging to deliver a complete and uninterrupted view of every acre and every field from planting to harvest.

      2. Technical overview - AI version

        AGMRI uses proprietary, patented technology to collect and analyze data from numerous sources. They gather high resolution aerial images, temperature readings, humidity measurements, rainfall, soil samples, terrain type, equipment utilized, planting rates, applications, and more. We then harness the power of hyperspectral analysis, Computer Vision (CV) and Deep Learning (DL) in order to identify patterns and build a complete and precise situational representation of every monitored field for the entire growing season.

        Evolving and up to date - The AGMRI Intelligence platform leverages hi-res aerial imagery, Computer Vision (CV) and Machine learning (ML) technologies to give you an ever-evolving, real-time picture of crop development every week, all season long.

        Continually learning – The revolutionary AI cognitive decision-making engine has already processed hundreds of terabytes of crop images across multiple seasons—it is smart and getting smarter. And the more data it processes, the better the algorithms get and the more accurate the results.

        Big Data - Our use of Big Data and AI enables a previously unattainable class of computation for Ag that uses self-learning algorithms to perform precise predictive analytics which remove the sampling errors typically associated with relying on scouting efforts alone.

      3. Added value thanks to Artificial Intelligence

        Their AI is capable not only of detecting when emergence occurs, but providing a full map of where it succeeded, struggled, and failed. It gives smart alerts pinpointing exactly where replant opportunities arise, giving the farmers quick and reliable insights into one of the most volatile periods of the farming process.

        Intelinair

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