Precision agriculture

In the general case, precision agriculture uses new technologies with the aim of increasing yields and profitability while reducing the amount of required resources. It can be as simple as a crop irrigation systems taking into account weather forecast, but could reach autonomous robots taking care of a wide range of tasks. While precision agriculture is not designed to tackle climate change, it opens up the opportunity to mitigate several sources of emissions.


 * More precise use of fertilizers can significantly reduce Nitrous Oxide emissions (which has 296 times the global warming potential of CO2).
 * Increasing crop yield can reduce the need of deforestation if the increased production does not simply transfer to more food waste or other Rebound effects.
 * Increasing soil carbon content by reducing the need for tillage and pesticides.
 * Enabling better agricultural practices such as regenerative agriculture.

While some precision agriculture technologies requires little to no machine learning, the usage of drones and field robotics calls for advanced machine learning techniques.

Journals

 * Computers and Electronics in Agriculture
 * Precision Agriculture

Workshops

 * Computer Vision for Agriculture, ICLR 2020

Groups and Labs

 * Vision for Agriculture
 * Australian Centre for Field Robotics

Kaggle datasets

 * Plant Seedlings Dataset
 * Agriculture crop images
 * Weed Detection in Soybean Crops
 * crop and weed detection data with bounding boxes
 * DeepWeedsX