Agriculture

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• New artificial intelligence algorithm better predicts corn yield | University of Illinois College of Agricultural, Consumer and Environmental Sciences
• What AI (Artificial Intelligence) Will Mean For The Cannabis Space | Tom Taulli - Forbes
• Machine learning helps robot harvest lettuce for the first time | Rich Haridy - New Atlas
• How a Japanese cucumber farmer is using deep learning and TensorFlow | Kaz Sato - Google
• Beyond the Hype: AI in Agtech | Allison Kopf
• Feeding the world with AI-driven agriculture innovation | Microsoft
• Artificial Intelligence in Agriculture | Mindtree
• Smarter Farms: Watson Decision Platform for Agriculture | IBM
• Sensors give farmers clear picture of animal health and well-being | Cargill
• At This High-Tech Farm, the Boss Is an AI-Powered Algorithm - Bowery Farming says its proprietary software can top the intuition of a seasoned farmer | Aki Ito
• New autonomous farm wants to produce food without human workers | Erin Winick
• A field‐tested robotic harvesting system for iceberg lettuce | S. Birrell, J. Hughes, J. Cai, and F. Iida
• Alibaba gets into farming – without getting its hands dirty | Rita Liao - TechInAsia
• AI for AG: Production machine learning for agriculture | Chris Padwick - Medium
• Landing AI - Computer Vision ... LandingLens enables new computer vision solutions to be introduced easily and rapidly, helping to increase throughput, cut costs, and improve quality.
• Soy Innovation Challenge Selects Four Finalists That Increase Value for Soybean Meal | The Soy Hopper ... POLARISqb utilizes quantum computing and artificial intelligence to revolutionize drug design. They are developing a feed additive, specifically a peptide, that makes soymeal feed digestible and nutritious for livestock without relying on costly extraction methods.
• I finally found a practical use for AI, and I may never garden the same way again | Lance Ulanoff - TechRadar

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 management involves monitoring and controlling the population of a particular species to maintain ecological balance and prevent overpopulation or endangerment. This can be achieved through species breeding programs to promote genetic diversity and species recognition techniques to accurately identify and track individuals.

• Species Breeding: enabling more precise and efficient methods to enhance genetic diversity and improve desirable traits in plants and animals. Through advanced algorithms and data analysis, AI can predict the most favorable breeding combinations based on genetic information, environmental factors, and specific breeding goals. In agriculture, AI-driven species breeding has resulted in the development of drought-resistant crops, disease-resistant varieties, and higher-yielding plants. In the field of livestock management, AI is utilized to identify superior genetic lines for breeding, leading to healthier and more productive animals. Moreover, AI-powered genetic tools have accelerated the breeding process, significantly reducing the time required to develop new breeds or crop varieties.
• Species Recognition: AI can analyze vast datasets of images, sounds, and biological data to distinguish between different species with a high level of precision. This capability finds applications in wildlife conservation, where AI-driven species recognition helps monitor endangered species and track their population dynamics in their natural habitats. Furthermore, AI-powered mobile apps have been developed to enable citizen scientists and nature enthusiasts to identify plants and animals they encounter in the wild, contributing to biodiversity monitoring efforts. In agriculture, species recognition allows farmers to detect and manage pests and diseases more effectively, safeguarding crops and minimizing the use of harmful pesticides.

Field Conditions Management

encompasses various strategies to optimize agricultural practices. Soil management involves soil testing, nutrient supplementation, and erosion control to ensure healthy and fertile soil. Water management focuses on efficient irrigation methods and water conservation practices to sustainably meet the needs of crops while minimizing wastage.

• Soil management: AI has emerged as a powerful tool in soil management, revolutionizing agricultural practices and environmental sustainability. Through the integration of various data sources, including satellite imagery, soil sensors, and weather forecasts, AI can offer real-time insights into soil health and fertility. By analyzing these data, AI algorithms can recommend precise and tailored fertilization schedules, optimizing nutrient application and reducing waste. Moreover, AI-driven soil management enables the identification of areas prone to erosion or nutrient depletion, allowing farmers to implement targeted erosion control and conservation practices. The technology also aids in precision irrigation, ensuring that water is distributed efficiently based on soil moisture levels and crop water requirements. Additionally, AI assists in soil carbon sequestration initiatives, helping to combat climate change by maximizing the soil's capacity to store carbon.
• Water Management: AI predicts water demand and availability, optimizing distribution and storage. Smart irrigation systems use real-time data to conserve water by delivering precise amounts to crops. AI also monitors water quality, rapidly detecting contaminants to protect public health and the environment. Furthermore, AI aids in leak detection, reducing water loss.

Crop Management

using advanced technologies and data analysis to enhance agricultural productivity. Yield prediction employs predictive modeling to estimate crop yields, enabling farmers to plan for optimal harvests. Crop quality assessment involves using sensors and machine learning to ensure crops meet specific standards for marketability and consumer satisfaction. Disease detection employs image recognition and data analysis to promptly identify and combat crop diseases. Weed detection uses computer vision and AI to differentiate between crops and weeds, allowing for targeted and eco-friendly weed control.

• Yield Prediction: By analyzing a vast array of data, including historical yield data, weather patterns, soil health, and crop growth stages, AI algorithms can generate precise yield forecasts for different crops and regions. This enables farmers to optimize their planting and harvesting schedules, plan for storage and transportation logistics, and make better-informed decisions on resource allocation. Yield prediction also plays a crucial role in risk management for agricultural businesses and insurance companies, allowing them to assess potential losses and develop appropriate coverage plans. The integration of AI-driven yield prediction enhances overall agricultural efficiency, reduces waste, and contributes to global food security by supporting more sustainable and productive farming practices.
• Crop Quality: AI has emerged as a vital tool in assessing and ensuring crop quality throughout the agricultural value chain. By harnessing advanced machine learning algorithms and computer vision techniques, AI can rapidly analyze vast quantities of data from imaging devices and sensors to evaluate various quality attributes of crops. This includes color, size, shape, texture, and other relevant characteristics. AI-powered systems can sort and grade crops according to quality standards, facilitating the production of consistent and high-quality produce for consumers. Additionally, AI is utilized to detect and classify defects, diseases, and pest damage, enabling early intervention and reducing crop losses. Beyond sorting and grading, AI-driven quality analysis also supports post-harvest storage and processing decisions, optimizing the allocation of resources and minimizing waste.
• Disease Detection: AI can support disease detection, revolutionizing healthcare and facilitating early diagnosis and treatment of various medical conditions. By leveraging advanced machine learning algorithms, AI can analyze vast amounts of patient data, including medical records, imaging scans, genetic information, and biomarker data. This enables AI systems to identify patterns and correlations that may not be apparent to human physicians, leading to more accurate and timely disease diagnoses. In fields such as radiology and pathology, AI-powered systems can assist in detecting abnormalities and potential signs of diseases, aiding healthcare professionals in making informed decisions. Furthermore, AI-driven wearable devices and remote monitoring systems can continuously analyze physiological data, allowing for real-time disease monitoring and timely intervention. The use of AI in disease detection holds great promise in improving patient outcomes, reducing healthcare costs, and advancing medical research and personalized treatment approaches.
• Weed Detection: By combining computer vision and machine learning, AI can accurately distinguish between crops and weeds in real-time. This enables farmers to implement precise and targeted weed control measures, reducing the reliance on herbicides and minimizing environmental impacts. AI-driven weed detection systems can be integrated into agricultural machinery, such as drones and autonomous robots, to survey large fields efficiently and identify weed-infested areas. As a result, farmers can adopt site-specific weed management strategies, optimizing resources and maximizing crop yields. Moreover, AI-powered weed detection can support organic farming practices by facilitating manual weed removal and reducing the need for synthetic herbicides.

Livestock Management

Involves overseeing the well-being and productivity of farm animals. Livestock production focuses on optimizing breeding, feeding, and housing practices to maximize the output of meat, milk, or other animal products. Animal welfare involves implementing measures to ensure the ethical treatment and health of the animals, including proper living conditions, veterinary care, and humane handling.

• Livestock Production: Through the integration of AI-driven data analytics and sensor technologies, livestock farmers can monitor and manage their animals more effectively. AI-powered systems can analyze data on animal behavior, health, and feed intake to identify early signs of illness or stress, enabling timely interventions and reducing the risk of disease outbreaks. Moreover, AI helps optimize breeding programs by analyzing genetic data to select superior breeding pairs, leading to improved traits and higher-quality offspring. In dairy and poultry farming, AI assists in optimizing feed formulation, ensuring that animals receive balanced and nutritious diets, resulting in increased milk production and egg yields.
• Animal Welfare: In agriculture and livestock management, AI-driven monitoring systems can continuously assess animals' well-being by analyzing their behavior, movement, and health data. This real-time analysis enables early detection of any signs of distress or illness, allowing for immediate intervention and veterinary care. Furthermore, AI supports animal enrichment programs by identifying and implementing personalized enrichment activities that cater to individual animals' preferences and needs. In wildlife conservation, AI-driven camera traps and drones help monitor and protect endangered species, reducing human interference and promoting a more sustainable coexistence with wildlife. Additionally, AI-powered voice and image recognition technologies aid in identifying and rescuing animals in distress, such as lost pets or injured wildlife. The integration of AI in animal welfare not only enhances animal care but also fosters greater understanding and compassion towards animals, driving positive change and better stewardship of the natural world.

AI Implementation

How ChatGPT is Being Used in the Field of Agriculture In this video, we take a deep dive into the world of financial planning. From creating a budget to investing for the future, we cover it all. Our experienced financial advisor shares insider tips and tricks to help you make the most of your money. Whether you're just starting out or looking to improve your current financial situation, this video has something for everyone. Don't miss out on the valuable information shared in this informative and easy-to-understand tutorial. Watch now to start taking control of your finances today!

Aquaculture 2.0 Powered by AI On today’s dojo.live we will be talking about "How are AI and Aquaculture playing an important role in feeding the world's growing population? Can new methods and technologies enable us to produce more with less?" with Stian Rognlid, CEO @ Aquaticode.

Artificial Intelligence in Agriculture Masterclass - Session 1 Claire Gormley from University College Dublin introduces some of the key challenges facing the agri-food sector and outlines how artificial intelligence is helping to tackle those challenges. Our first speaker is Jerome Bindelle from University of Liege discusses the use of drones in precision agriculture and he is followed by our second speaker Marek Kraft from Poznan University who talks about using robots for hemp cultivation.

Artificial Intelligence in agriculture | Yasir Khokhar | E Tipu 2022 Yasir Khokhar is an agribusiness pioneer with a chronic habit of ‘swimming upstream’ and finding like-minded people to solve problems of consequence. He's the CEO and founder of Connecterra, which aims to make global agriculture more productive, humane and sustainable using sensors and machine learning. At E Tipu 2022: the Boma Agri Summit, he details the evolution of AI and its widespread potential at a grassroots farm level. "Almost every aspect of a farm has an AI-driven use case." “There has never been a better time to build, and AI can enable progress in this industry.”

Data-Centric Zero-Shot Learning for Precision Agriculture with Dimitris Zermas - 615 Today we’re joined by Dimitris Zermas, a principal scientist at agriscience company Sentera. Dimitris’ work at Sentera is focused on developing tools for precision agriculture using machine learning, including hardware like cameras and sensors, as well as ML models for analyzing the vast amount of data they acquire. We explore some specific use cases for machine learning, including plant counting, the challenges of working with classical computer vision techniques, database management, and data annotation. We also discuss their use of approaches like zero-shot learning and how they’ve taken advantage of a data-centric mindset when building a better, more cost-efficient product.

ICICLE Seminar Series The AgAID Institute: Tackling challenges in agriculture through AI innovations Ananth Kalyanaraman, Professor and Boeing Centennial Chair in Computer Science at Washington State University, to present "The AgAID Institute: Tackling the 21st century challenges in agriculture through AI innovations"

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.

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

Tractor

• Robotics ... Vehicles ... Drones ... 3D Model ... 3D Simulation Environments ... Simulated Environment Learning ... Point Cloud

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What if a farmer didn't just manage a field, but every seed and plant instead? - Precision Ag Technology - John Deere

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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.

8R Tractor | John Deere

• 8R Series Tractors | John Deere
• John Deere Reveals Fully Autonomous Tractor at CES 2022
• John Deere’s self-driving tractor lets farmers leave the cab | The Verge
• Autonomous Electric Tractor Brings AI to the Field | Mathworks

John Deere's fully autonomous tractor: was revealed at CES 2022 and combines Deere's 8R tractor, TruSet-enabled chisel plow, GPS guidance system, and new advanced technologies It can operate without a driver in the cab or in the field, and can detect and avoid obstacles using six pairs of stereo cameras and a deep neural network. It can also be monitored and controlled remotely using John Deere Operations Center Mobile.

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Self-driving tractors could help save farmers money and automate work that is threatened by an ongoing agricultural labor shortage. - Wired

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The fully autonomous 8R relies on neural network algorithms to make sense of the information streaming into its cameras. Deere has been collecting and annotating the data needed to train these algorithms for several years, Hindman says.A similar AI approach is being used by companies building self-driving cars. Tesla, for example, gathers data via its cars that is used to hone its Autopilot self-driving system. - * John Deere's Self-Driving Tractor Stirs Debate on AI in Farming | Will Knight ... The automation, and control of the resulting data, raises questions about the role of human farmers.

'https://www.roboticgizmos.com/wp-content/uploads/2022/01/10/John-Deeres-Autonomous-8R-Tractor.gif'

Monarch Tractor

• Monarch Tractor ... 100% Electric | Driver Optional | Data-Driven

This tractor is an electric, driver-optional, smart machine that can perform various tasks such as plowing, harvesting, spraying, and more. It can also collect and analyze data about the soil, crops, weather, and pests using sensors and cameras. It can reduce greenhouse gas emissions by 77% compared to diesel tractors.

Naïo Technologies' Dino Robot

• Naïo Technologies
  • Delivering agricultural robotic solutions

This robot is a self-driving weeding machine that can work autonomously in vegetable fields. It uses cameras, lidars, GPS, and AI to recognize crops and weeds, and can apply mechanical or electrical weeding methods⁴. It can also adapt to different crops and terrains, and can be controlled via a web platform.

Blockchain, AI and Agriculture

• Blockchain

VeChain Blockchain Technology will disrupt the Cannabis Industry How VeChain will disrupt the legal cannabis industry and how you can benefit from it, no matter if you are a crypto investor, a cannabis user, or even an entrepreneur looking to open or improve a business using blockchain technology for Supply Chain Management solutions of Perishable Goods.

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 ➜ https://t2m.io/FutureBridge