Jump to: navigation, search

Youtube search... ...Google search

Though still in the beginning of its journey, ML-driven farms are already evolving into artificial intelligence systems. At present, machine learning solutions tackle individual problems, but with further integration of automated data recording, data analysis, machine learning, and decision-making into an interconnected system, farming practices would change into with the so-called knowledge-based agriculture that would be able to increase production levels and products quality. Machine Learning in Agriculture: Applications and Techniques | Sciforce

  • Species management
    • Species Breeding
    • Species Recognition
  • Field conditions management
    • Soil management
    • Water Management
  • Crop management
    • Yield Prediction
    • Crop Quality
    • Disease Detection
    • Weed Detection
  • Livestock management
    • Livestock Production
    • Animal Welfare

Powering the Future of Agriculture through Google Solutions (Cloud Next '18)
Artificial intelligence is making a significant impact on nearly every industry, and agriculture is no different. Google’s tools are working together to improve the world’s food supply. From the Cloud to Glass, farmers now have millions of vital images and data points available to them within seconds. In this session, you’ll learn how products such as the Google Cloud Platform, TensorFlow and AutoML are working in the field, as well as how you can use them across any industry to make a profound difference for your business.

Microsoft and ICRISAT - Bringing Artificial Intelligence to agriculture to boost crop yield
Following the launch of the pilot in June 2016, that tested a new Sowing Application for farmers combined with a Personalized Village Advisory Dashboard for the Indian state of Andhra Pradesh, the results show a 30% higher average in yield per hectare. The Sowing App was developed to help farmers achieve optimal harvests by advising on the best time to sow depending on weather conditions, soil and other indicators. The pilot was implemented in Devanakonda Mandal in Kurnool district and the advisory applied only to the groundnut crop.

Artificial intelligence could revolutionize farming industry
Agriculture in the U.S. is in trouble: American farmers are getting older, with their average age just over 58. As farming in general faces a labor shortage, growers are now trying to find a solution with the help of AI technology from Silicon Valley. Errol Barnett reports.

AI and the future of agriculture
Simon Jordan, Robotics & Control Lead, explains how agriculture will benefit from advances in machine vision and AI. New technologies are making huge steps forward, enabling machines to be adaptable and treat plants at an individual level by recognising shapes and texture. From counting apples and estimating yields to identifying weeds in crops, machines are getting smarter.

Artificial Intelligence: Smart Machines for Weed Control and Beyond
Emerging technologies such as artificial intelligence, computer vision and robotics are just beginning to be integrated into production agriculture. These technologies promise to enable the next wave of precision agriculture by moving from zone management to plant management. Learn about the opportunities and challenges of managing every plant, and how Blue River Technology is utilizing these technologies to deploy See & Spray machines that apply herbicide only to weeds. Presented at the 2017 InfoAg conference in St. Louis, Missouri by Ben Chostner, VP Business Development for Blue River Technology.

Webinar Precision Agriculture Maximize quality and productivity with cutting-edge AI
In this webinar, we explored how your daily field operations can benefit from AI-powered object detection and mapping and by that, greatly reduce the time to analysis & interpret data. From localizing diseases and invasive species to measure the per-parcel crop density you will know how to master the creation of detectors adapted to precision agriculture. Watch the recording and explore the potentials of Picterra in maximizing your working quality and productivity.

Using Machine Learning to Reduce World Hunger (Sponsored by Microsoft) - Jennifer Marsman
O'Reilly - iterative thinking process

Next Generation of Food & Agriculture Technologies : A.I. Powered Agriculture, with Caleb Harper
Hello Tomorrow Global Summit 2016

Using artificial intelligence to save bees
A beekeeper teamed up with the Signal Processing Laboratory 5 and a group of EPFL students to develop an app that counts the number of Varroa mites in beehives. This parasite is one of the two main threats – along with pesticides – to bees’ long-term survival. Knowing the extent of the mites’ infestation will allow beekeepers to protect their bees more effectively.

Artificial intelligence and agriculture
ViVet Innovation Symposium talks 2019 Dr Matthew Smith from Microsoft Digital, explaining the role of artificial intelligence in agriculture.

2019: Long-term projections of soil moisture using deep learning and SMAP data
CUAHSI's 2019 Spring Cyberseminar Series on Recent advances in big data machine learning in Hydrology Date: April 19, 2019 Topic: Long-term projections of soil moisture using deep learning and SMAP data with aleatoric and epistemic uncertainty estimates Presenter: Chaopeng Shen, Pennsylvania State University

Application of AI technology in Agriculture as a example Banana AI app (Tumaini) By Dr.S.Elayabalan
Application of AI technology in Agriculture as a example; AI technology for banana pest and disease detection (Mobile, Drone, and Satelite technology) Acknowledge to CIAT-Bioversity International.

The Future of Farming with AI: Truly Organic at Scale
As climate change and global demographics begin to put excessive strain on the traditional farming model, the need for an agriculturally intelligent solution is vital. By 2050, the world population will increase by over 2 billion people. Current crop yields and freshwater resources will not be sufficient to sustain a population over 9 billion people. On May 15th 2017, the Machine Learning Society hosted this event to showcase high tech farming techniques used in vertical and urban farming. Our keynote speaker is Ryan Hooks of Huxley. Huxley uses computer vision, augmented reality (AR), and A.I. to greatly improve yield, while driving the down cost and resources requirements. Huxley is creating an “operating system for plants” to grow with 95% less water, 2x the speed, 2/3 less carbon output, and half the nutrients needed. Come to our event to learn more. Ryan Hooks, CEO & Founder, Huxley Ryan Hooks has spent the past decade creating identities for companies such as Google, UNICEF, and Vevo. Working in the media space, he has helped communicate food, water, population, and resource issues via Food Inc, the G8 summit, and other organizations. He has been featured in FastCompany Magazine. In 2013, he entered the tech world with Avbl to help creative talent connect in real-time. In 2014, he founded Isabel, a smart grow system for the growth and transportation of deliciously efficient produce. Debuted on Summit at Sea 2016: Plant Vision™ by Huxley utilizes computer vision, AI, machine learning, and Augmented Reality to radically transform the way we grow. Agritecture is a blog and workshop platform all about growing food in our cities. The blog promotes fresh, unique, and high preforming urban agriculture design concepts; and juxtapose them against real research and businesses in urban agriculture. AeroFarms is on a mission to transform agriculture by building and operating environmentally responsible farms throughout the world to enable local production at scale and nourish our communities with safe, nutritious, and delicious food. AeroFarms disrupts traditional supply chains by building farms on major distribution routes and near population centers. The company defies traditional growing seasons by enabling local farming at commercial scale all-year round. They set new standards for traceability by managing greens from seed to package. They do it all while using 95% less water than field farmed-food and with yields 130 times higher per square foot annually. Recently AeroFarms has been featured on CBS News and CNN.

The future outlook for Agriculture through the use of AI and Agri-robotics
The Face of Agriculture is changing – How will AgriTech support you & are you ready to realise its full potential? Today the Fourth Industrial Revolution is bringing exciting opportunities for farmers to increase productivity, protect the environment and make farming safer. From the use of ‘big data’ and AI to inform farm management decisions, to autonomous tractors and robotic pickers. We are on the cusp of innovation where farmers and growers can minutely manage inputs to maximise production, and use automation and robotics to reduce labour numbers and costs. The government has committed a £90million investment supporting the idea that Agritech is a burgeoning market (Transforming Food Production Challenge through the Industrial Strategy) We are proud to be exploring the use of robotics in this important sector, employing almost 4 million people and larger than the automotive and aerospace sectors combined. Agritech companies are already working closely with UK farmers, using technology, particularly robotics and AI, to help create new technologies and herald new innovations. This is a truly exciting time for the industry as there is a growing recognition that the significant challenges facing global agriculture represent unique opportunities for innovation, investment and commercial growth Wendy Hewitson - AgriTech Programme Manager ,will present an overview of the Eagle Labs and the AgriTech Programme Prof Simon Pearson, Director of LIAT/Professor of Agri-Food Technology Simon’s expertise covers a diverse range of agri technology applications including robotic systems, automation, energy control and management, food safety systems, novel crop development The environmental physiology of fresh produce and ornamental crops, including impacts on crop quality and development; The post-harvest physiology of vegetables, fruits and cut flowers, including the use of modified atmosphere packaging; The effects of light manipulation on crop growth and development, including the development and application of greenhouse spectral filters and LED lighting systems; The development of on farm decision support systems from remote sensing information; The development of pre and post farm gate supply and demand forecasting systems Mihai Ciobanu – CEO Fresh4Cast "Using AI to improve predictability in fresh fruit & veg" Mihai is an experienced business builder and data scientist , who’s working with a talented team at Fresh4Cast to provide accessible solutions in agriculture. He saw a lack of predictability in the sector and thus volatility, inefficiencies and significant waste and wanted to build and economic solution. Fresh4Cast brings together all the relevant data streams, internal and external, into an innovative and simple user interface. That’s why they develop prediction models and deliver automated forecasts for business critical processes. Their innovative intelligence solutions and predictive tools assist growers and distributors of fresh produce in the decision making process. As the weather becomes more unpredictable and the economy more global, growers and distributors need an efficient way to understand these influences fast. Having the right metrics can transform a business.

VIRTUAL SEMINAR: Artificial Intelligence for Agricultural Intelligence Professor Richard Xu
Agriculture is by far, one of the oldest industries in the world. Just like any other industries that modern-day artificial intelligence (AI) technologies have helped to transform, AI has also found its way into many agricultural applications. Typical examples have included machine learning-based data decision making, forecasting to help to increase productivity and quality control; Computer vision based drone/robotic precision spraying etc. Recently, many new AI technologies have emerged and are eagerly awaiting for its agricultural usefulness. For example, we may apply Reinforcement Learning (RL), which is technology helps DeepMind to win chess/Go game, to compute the best farming strategy/policy at a given time, to overcome the harsh farming environment adversarially. In this talk, we will introduce and demystify some of the AI concepts. We will also showcase some of the standard tools available to tackle AI problem as well as some the exciting new AI research happening in other disciplines that can be readily applied to agricultural applications! Richard Yi Da Xu: is an Associate Professor in Machine Learning at the University of Technology, Sydney (UTS). He leads a team of 30 people, includes postdoc, PhD students, and data engineers; His primary research is in Bayesian machine learning, Deep Learning and Computer vision. He published at many International conferences, including AAAI, ECAI, IJCAI, and AI-STATS as well as many top IEEE Transactions: IEEE-(TNNLS, TIP, TSP, TKDE, and T-Cybernetics). Since 2009, he published 1500+ slides of PhD training material in machine learning as well as many online ML videos. His team has collaborated with many Australian industries, including banks, e-commerce, government, utilities, defence and law firms; He established a Deep Learning Sydney meetup which has 4400+ members. He is the sole Australian representative to attend ISO JTC1 SC42 (Artificial Intelligence)’s first plenary. FOOD AGILITY CRC: Food Agility is a $150m Innovation Hub creating a sustainable food future for Australian producers, consumers, and communities. We curate and invest in cooperative research focused on finding digital solutions for the Australian agrifood sector.

How Tech-Startup May Play an Important Role in Smart Agriculture
In recent years, we are seeing more real-life cases across the global platform that tech-startups began to play a vital role within the smart agricultural sector, including Taiwan. Smart agriculture concentrates on combining modern technologies and farming management to advance both the quantity and quality of agricultural production. Over the years, the innovation of farming technology allows farmers to monitor the growth of plantation through artificial satellites digitally, however, with the newly designed Smart Farming origination; it is possible to observe agriculture production through inexpensive and revolution smart solutions. The introduction of drones has been used in multiple areas such as research, journalism, recreational use, and defense in the current worldwide market. Furthermore, with the nowadays IoT and AI drone technology, it is achievable to observe the water content and harmful insects in order to maintain the healthiness and freshness of the plant for consumers. Taipei Computer Association (TCA) is delighted to partner up with InnoVEX, COMPUTEX, Taiwan IOT Technology and Industry Association (TwIoTA), Startup Island Taiwan to support and Asia Silicon Valley Development Agency(ASVDA) to organize this webinar discussing technology and smart agriculture. We hope that this content may bring you new ideas on how we can do better for the agriculture industry!

Crop Detection from Satellite Imagery using Deep Learning - Part One
In this video, Karim Amer presents on "Crop Detection from Satellite Imagery Using Deep Learning" at our Weekend Webinar. This is a result of his winning solution of a machine learning challenge on #zindi found here Presentation Find his GitHub repository here Moderator: John Bagiliko

Implementation of Deep Learning in Agriculture Crop Identification
e-farmerce Platform

Blockchain, AI and Agriculture

Using Blockchain Technology to Improve the Cannabis Supply Chain
Supply chain is often said to be one of the best use cases for blockchain technology and so I was especially excited to sit down with Dr. Isaac Balbin the Founder of PARSL to learn more about how they are utilizing blockchain technology to improve the cannabis supply chain. This is the first time I’ve had someone from the cannabis industry on my channel and so I was really excited to learn more about how these two emerging industries are finding ways to co-exist. We talk through the process of how PARSL works and the Smart Tags they use to bring more transparency of sourcing and different strains. Isaac talks about the issues of misrepresentation in terms of what the end consumer or patient is getting and how it’s not always accurate. This presents a large problem because different strains impact people in different ways and patients depend on the end product being correct.

AgroBlock | Transforming Agro Businesses with Blockchain | Artificial Intelligence
Global demand for agriculture and food produce is going to increase by 69% to feed 9.6 Billion people by 2050. How can emerging technologies help growers and consumers to collaborate & build a quality centric and trustworthy system to meet this demand in a sustainable way. Introducing AgroBlock !!! a combination of Internet of Things, Artificial Intelligence, and Blockchain components to enable circular economy in agriculture. Agroblock helps farmers to manage the vital parameters of soil & water at the micronutrient level. On the other side, consumers are benefited in the form of reliable quality assurance. The Artificial intelligence block, adds knowledge in the form of crop advisory to farmers and prediction of market dynamics to retailers. Finally, Agroblock enables trust and revenue assurance between growers and consumers using blockchain and smart contracting. Agroblock empowers growers and consumers through a unified agro supply chain that ensures food information accessibility and transparency via trust, knowledge, and reliability.

Next Generation Supply Chain Driven by Blockchain
Trimble Transportation Enterprise Solutions, a leading provider of enterprise software to over 2,000 transportation and logistics companies, and the largest data science organization in the industry, supports thousands of customers, cabs, and trailers on the road. The freight and transportation industry today relies on inherently manual systems, where spreadsheets and individual files are updated by each party every time a new action is required. This causes a lack of accountability and zero visibility into important data stored in file-based systems. Trimble has developed several blockchain-based applications to give its customers a better platform experience. They are now joining their ecosystem by bringing together a way to engage in a single unified platform. Trimble will unveil a new Transportation Management System as a Service, called Harmony, built on a blockchain architecture powered by Apache Kafka and Apache NiFi. Trimble has designed an architecture that leverages Hortonworks Big Data solutions, HDP, HDF and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships The platform provides visibility for all participants across the entire Pick-Pack-Ship and Order-to-Cash processes Apache NiFi acts as the key data ingestion/management layer that determines which data stays off-chain for advanced analytics and which data goes on-chain for complete immutability and auditability. Speaker: TIMOTHY LEONARD EVP Operations & CTO at TMW Systems (A Trimble Company)

Implementation of Blockchain in Agri Supply Chain
Anjum Iqbal: Blockchain 101 Ashar Ahmed: Agri Supply Chain Use Case Omer Ahmed Khan: Avanza partnership with Govt. of Sindh (Pakistan), through SAGP to launch a Blockchain Based Procurement Platform Aamer Hayat Bhandara: What will be the grassroot level impact and how the small scale farmers can benefit from these technologies?

Blockchain and AI for Efficient Public Service Delivery
by Ananta Center and KPMG Streamed live on Dec 17, 2018

Future Food: 5G, Blockchain, and Artificial Intelligence | Disruption Decade Podcast
We discuss the future of food technology: How the controversial 5G will improve the food supply chain, Blockchain use by Mastercard, and the use of Artificial Intelligence in the food industry. Guest: Sarah Browner - Analyst, Food & Nutrition at FutureBridge Future Bridge website ➜