New global land use mapping, jet stream led forecasting and novel ag insurance to off-set price volatility to be discussed at Agri-TechE event 'De-risking Agriculture Through Weather-Tech'
AI will be key in solving one of the biggest puzzles facing humanity - feeding our growing population in the face of over-population and climate change.
Slow and steady is winning the race as Farmwave's platform has taken almost 6 years to get to this release... but the AI output results and complex core in the background combined with an easy and almost social style UI/UX form of data collection is on point.
JetSeed Granule Spreading System is designed to dispense granules such as seeds, fertilisers and pesticides precisely and effectively to any environment through high-speed airflows. It helps to combat grassland degradation...
With many on-farm processes being repetitive, occurring at inconvenient times of the day or night, and having adverse effects on worker health and safety, the agricultural sector has the opportunity to develop and use autonomous machinery on-farm.
Models and data analytics not only recap what is already occurring between water and plants across expansive rows of corn, they can actually predict what will come in the hours, days and weeks ahead.
Although they promise streamlined farming operations, higher yields and greater costs savings for growers, how much of AI and machine learning in agriculture today is more hype than reality?
Consus has developed an intelligent software-based system that reduces the burden of audits and improves productivity. By linking HR records to labour deployment the facial recognition module enables accurate costing of all products and improved traceability.
Plant breeding has been going on for 10,000 years but technology -unmanned aerial vehicles (UAVs), robots, artificial intelligence (AI) and machine learning -is revolutionizing the practice.
Do you have a problem that AI might be able to solve? If so, have you started collecting the data that might be needed? The sooner you get started, the sooner you might be able to see the benefits.
For instance, our greenhouse clients can obtain a 50% to 70% reduction in error in harvest forecasts from the start; following a year of further learning, the AI technology can reduce errors by more than 70%.
Using Motorleafs artificial intelligence and machine-learning algorithms, the digital agronomist software can acquire data from indoor growing conditions. In turn, the algorithms learn growing patterns in the greenhouse, which then can predict the size of future harvests.
European software developer 'Cognitive Technologies has developed the world's first industrial agrodroid for international agricultural market.
There are many different types of potential applications of AI in agriculture -- ranging from machine control, to image analysis, to improving our understanding of where, when, and how a crop might respond toparticular environmental conditions.
To build a robust deep learning model, it can be much more than training or fine tuning some existing models (e.g. inception v3, resnet, LSTM, etc.) with your own dataset. These winning models are your best friend and can usually serve as base models.
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Artificial intelligence can be used, for example, to classify fruit varieties or to identify damaged parts (e.g. apples with marks or colour deviations). To cover all possible variances with classical image processing would be very time-consuming and costly. AI is able to solve these challenges in no time at all. With IDS NXT ocean, there is now a user-friendly complete solution for industrial applications available. It requires neither special knowledge in deep learning nor camera programming.