Difference between revisions of "Agriculture"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=Agriculture+farm+artificial+intelligence+deep+learning Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Agriculture+farm+deep+machine+learning+ML ...Google search] |
* [[Case Studies]] | * [[Case Studies]] | ||
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* [[Image Classification]] | * [[Image Classification]] | ||
* [[Blockchain]] | * [[Blockchain]] | ||
| − | * [ | + | * [https://deepindex.org/#Agriculture Deepindex.org list] |
| − | * [ | + | * [https://www.sciencedaily.com/releases/2020/02/200220130500.htm New artificial intelligence algorithm better predicts corn yield | University of Illinois College of Agricultural, Consumer and Environmental Sciences] |
| − | * [ | + | * [https://www.forbes.com/sites/tomtaulli/2019/09/08/what-ai-artificial-intelligence-will-mean-for-the-cannabis-space/#5ce8f5a344a2 What AI (Artificial Intelligence) Will Mean For The Cannabis Space | Tom Taulli - Forbes] |
| − | * [ | + | * [https://newatlas.com/robot-harvest-lettuce-vegetable-machine-learning-agriculture/60465/ Machine learning helps robot harvest lettuce for the first time | Rich Haridy - New Atlas] |
| − | * [ | + | * [https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow How a Japanese cucumber farmer is using deep learning and TensorFlow | Kaz Sato - Google] |
| − | * [ | + | * [https://medium.com/agrilyst/beyond-the-hype-ai-in-agtech-b646e5eae1f0 Beyond the Hype: AI in Agtech | Allison Kopf] |
| − | * [ | + | * [https://cloudblogs.microsoft.com/2018/11/29/feeding-the-world-with-ai-driven-agriculture-innovation/ Feeding the world with AI-driven agriculture innovation | Microsoft] |
| − | * [ | + | * [https://www.mindtree.com/sites/default/files/2018-04/Artificial%20Intelligence%20in%20Agriculture.pdf Artificial Intelligence in Agriculture | Mindtree] |
| − | * [ | + | * [https://www.ibm.com/blogs/research/2018/09/smarter-farms-agriculture/ Smarter Farms: Watson Decision Platform for Agriculture | IBM] |
| − | * [ | + | * [https://www.cargill.com/2018/cargill-brings-facial-recognition-capability-to-farmers Sensors give farmers clear picture of animal health and well-being | Cargill] |
| − | * [ | + | * [https://www.bloomberg.com/news/articles/2018-09-20/at-this-high-tech-farm-the-boss-is-an-ai-powered-algorithm 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] |
| − | * [ | + | * [https://www.technologyreview.com/s/612230/new-autonomous-farm-wants-to-produce-food-without-human-workers New autonomous farm wants to produce food without human workers | Erin Winick] |
| − | * [ | + | * [https://onlinelibrary.wiley.com/doi/full/10.1002/rob.21888 A field‐tested robotic harvesting system for iceberg lettuce | S. Birrell, J. Hughes, J. Cai, and F. Iida] |
| − | * [ | + | * [https://www.techinasia.com/alibaba-ai-et-brain-agriculture Alibaba gets into farming – without getting its hands dirty | Rita Liao - TechInAsia] |
| − | * [ | + | * [https://medium.com/pytorch/ai-for-ag-production-machine-learning-for-agriculture-e8cfdb9849a1 AI for AG: Production machine learning for agriculture | Chris Padwick - Medium] |
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. | 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. | ||
| − | [ | + | [https://www.kdnuggets.com/2019/05/machine-learning-agriculture-applications-techniques.html Machine Learning in Agriculture: Applications and Techniques | Sciforce] |
* Species management | * Species management | ||
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** Animal Welfare | ** Animal Welfare | ||
| − | <img src=" | + | <img src="https://wol-prod-cdn.literatumonline.com/cms/attachment/aecca34d-bb14-4bd2-831d-5fd346e097fe/rob21888-fig-0006-m.jpg" width="800"> |
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| + | <b>Peter Zeihan at🌾FARMCON 2023 - Conference for Creative Minds in Agriculture | ||
| + | </b><br>FARMCON is an opportunity for you to connect with like-minded movers and shakers in the ag sector. These are people who have felt the ground crumbling beneath their feet and lived to tell the tale. Peter Zeihan is a geopolitical strategist: the study of how people and places impact financial, economic, cultural, political and military developments. Zeihan is also an award-winning author with NY Times Best Seller, "The End of the World is Just the Beginning". This book maps out the next world: a world where countries or regions will have no choice but to make their own goods, grow their own food, secure their own energy, fight their own battles, and do it all with populations that are both shrinking and aging. With Russia's latest military moves and all of the uncertainty with China, I thought there was no better time to hear from Zeihan and take a look through his lens. Remember, with any business, you need to get the larger macro view correct. This is a must for gaining a better understanding of the larger macro picture! | ||
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| + | <b>ICICLE Seminar Series The AgAID Institute: Tackling challenges in agriculture through AI innovations | ||
| + | </b><br>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" | ||
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<youtube>fEv_YsVkXiU</youtube> | <youtube>fEv_YsVkXiU</youtube> | ||
<b>Powering the Future of Agriculture through Google Solutions (Cloud Next '18) | <b>Powering the Future of Agriculture through Google Solutions (Cloud Next '18) | ||
| − | </b><br>Artificial intelligence is making a significant impact on nearly every industry, and agriculture is no different. | + | </b><br>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. |
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<b>Crop Detection from Satellite Imagery using Deep Learning - Part One | <b>Crop Detection from Satellite Imagery using Deep Learning - Part One | ||
| − | </b><br>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 [ | + | </b><br>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 [https://zindi.africa/competitions/iclr-workshop-challenge-2-radiant-earth-computer-vision-for-crop-recognition here] [https://docs.google.com/presentation/d/1Wr05On3m0LGylMwKUZ77HVCZwDkpt4qvtKgmO0aCraY/edit#slide=id.p Presentation] Find his [https://github.com/karimmamer/CropDetectionDL GitHub repository here] Moderator: John Bagiliko |
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Revision as of 12:47, 19 February 2023
Youtube search... ...Google search
- Case Studies
- Autonomous Drones
- Satellite Imagery
- Global Positioning System (GPS)
- Microbiome and Metagenome Analysis
- Image Classification
- Blockchain
- Deepindex.org list
- 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
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
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Blockchain, AI and Agriculture
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