Agriculture

This page is about the intersection of agriculture and machine learning in the context of climate change mitigation. For an overview of agriculture as a whole, please see the Wikipedia page on this topic.

A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.

As described in the paper "Tackling Climate Change with Machine Learning":

Agriculture is responsible for about 14% of GHG emissions. This might come as a surprise, since plants take up CO₂ from the air. However, modern industrial agriculture involves more than just growing plants. First, the land is stripped of trees, releasing carbon sequestered there. Second, the process of tilling exposes topsoil to the air, thereby releasing carbon that had been bound in soil aggregates and disrupting organisms in the soil that contribute to sequestration. Finally, because such farming practices strip soil of nutrients, nitrogen-based fertilizers must be added back to the system. Synthesizing these fertilizers consumes massive amounts of energy, about 2% of global energy consumption. Moreover, while some of this nitrogen is absorbed by plants or retained in the soil, some is converted to nitrous oxide, a greenhouse gas that is about 300 times more potent than CO₂.

Such industrial agriculture approaches are ultimately based on making farmland more uniform and predictable. This allows it to be managed at scale using basic automation tools like tractors, but can be both more destructive and less productive than approaches that work with the natural heterogeneity of land and crops. Increasingly, there is demand for sophisticated tools which would allow farmers to work at scale, but adapt to what the land needs. This approach is often known as “precision agriculture.”

Machine Learning Application Areas

Precision agriculture

Main article: Precision agriculture

This can increase the long term yield, reduce the need for tillage and fertilizers.

• Weed and pest detection
• Soil sensing
• Farming assistant
• Autonomous farming

Monitoring agriculture

Main article: Monitoring agriculture

This helps studying trends and encourage good practices

• Remote sensing of soil composition
• Estimating emissions of methane (CH₄) and nitrous oxide (N₂O)
• Segmentation and classification of crops
• Quantifying cattles and other farm animals

Background Readings

• Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices
• Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Community

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

Library and Tools

Some packages for working with remote sensing data are,

• eo-learn: A python package maintained by the European Space Agency, giving easy access to imagery from Sentinel satellites, as well as utilities for data processing

Data

Remote sensing

Remote Sensing Datasets offers great opportunity to monitor agriculture and can be georeferenced to match ground measurements. In particular, Google earth engine offers a convenient interface over freely available satellite imagery such as Landsat and Sentinel.

Remote Crop Identification

• Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis

Kaggle datasets

• Plant Seedlings Dataset
• Agriculture crop images
• Global Food & Agriculture Statistics
• Weed Detection in Soybean Crops
• crop and weed detection data with bounding boxes
• DeepWeedsX
• California Crop Mapping - 2014