Challenge background
Severely depleted soils need to provide food for an ever-growing global population and 1/3 of the food produced remains never eaten. AI offers multiple opportunities to make farming smarter. One of those opportunities is to help farmers know where to add water or fertilizer using data such as soil PH, temperature, and moisture levels, combined with other data sources.
The problem
The ability of agricultural equipment to help actors better think, predict, and advise farmers via a variety of AI applications helps to achieve food security in the Netherlands. Given the enormous impact of climate change, having a machine learning model will limit these impacts.
The result should not only be an improvement on the previously provided challenge (https://omdena.com/projects/foodsecurity-ai/), but should also give a more detailed result on which crops are best to farm on a specific field.
Goal of the project
- Detecting problems in fields.
- Use of satellite images.
- Use of weather forecasting.
- Soil health monitoring system.
- Analyzing crop health by satellite images.
- AI-enabled system to detect pests.
What you'll learn
1. Data extraction (Google Earth Engine)
2. Processing images
3. Data visualization
4. Machine Learning/ Neural Networks