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 Motorleaf's 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.
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.
If 29-year-old me were here in the present day looking at what's going on in agriculture, I can't help but wonder what would be going through my mind-"Do I believe what I am seeing?" or "Am I inside a Sci-Fi movie?"
With Agrilyst, growers can understand which of their crops are the highest performers based on yield, growth rate, or both and run production scenarios with higher performers having more space allocation. These recommendations help growers optimize the space on their farm and drive significantly higher revenues.
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