Precision Improvement Agriculture through precision with ML/AI with Applied Materials

Industry Partner

Sushil D. Padiyar, Senior Director at Applied Materials

Precision Improvement Agriculture through precision with ML/AI with Applied Materials

Pre-existing research shows that spectral imaging analysis detects disease and stress symptoms in plants before physical signs starting to show. The need of development of low cost on chip spectral camera modules generated our decision to focus on incorporating such a sensor in a IOT device. The MVP of Precision is that agriculture accounts for 70% of worldwide water use, so water usage must be optimized first, while incorporating hyperspectral imaging and SiVM (simple volume maximisation) to identify hydration stress in plants while the model determines a suitable water cycle and signs to make which adjustments.The biggest salvation for the team was changing the scale of the project to suit greenhouse farmers and those local to the US with more capital to test the project and gather data as well as efficacy. They used a CAD design software to create an engineering model and a graphical flow chart tool to create a ML functional flow diagram as well as Google's Jamboard to create the task list for the software structure.

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