How can Earth Observation Solutions be Improved in Agriculture with Machine Learning

While Earth Observation satellites help capture a large amount of data in the form of Earth imagery, Machine Learning helps study these images and identify croplands and agricultural fields at any scale.

How can Earth Observation Solutions be Improved in Agriculture with Machine Learning
Image: Google Images

How can Earth Observation Solutions be Improved in Agriculture with Machine Learning

Machine Learning has played a vital role in agricultural development worldwide. It has helped identify and study patterns of land use and also improve the utilization of resources.

Machine Learning and Earth Observation are technologies that are powerful alone, but when worked in conjunction, they complement each other very well. While Earth Observation helps create powerful images of the earth to help understand the changes at a larger scale, Machine Learning helps study these changes and analyze large amounts of data to improve upon existing EO modes and solutions. 

Without Machine Learning, it would have been a complex and cumbersome task to come up with newer models of Earth Observation that were not only sophisticated but also increased efficiency and helped a deeper study of agriculture. 

While Earth Observation satellites help capture a large amount of data in the form of Earth imagery, Machine Learning helps study these images and identify croplands and agricultural fields at any scale. These croplands are mapped at a local, regional, and even a continental level to understand land patterns and help policymakers and agriculture professionals, including farmers to gain an insight into what they are working with.

These technologies can help estimate outputs, understand future agricultural patterns, analyze the impact of pesticides and chemicals, help with disease outbreaks in crops, and also help find out the seasons where crop yield is high.

All of these benefits of Earth Observation being used with Machine Learning can help address more significant problems at a much larger scale. Issues such as food security, pressure on environment and land, and even employment in agriculture. These insights provided by Machine Learning and Earth Observation can help create awareness and spread information amongst farmers. 

With these real-time insights and analyses, farmers can be given recommendations by policymakers and stakeholders. Governments can also plan their support and aid for farmers and the agricultural sector with the help of this valuable data. All of this information and aid can help farmers increase their yield productivity, crop quality, and in turn, protect their livelihoods.

If the use of Earth Observation and Machine Learning needs to be leveraged at its full capacity in agriculture, some fundamental shifts are needed. Challenges in the system need to be addressed adequately to use technologies for holistic agricultural development at a global level.

The first challenge that needs attention is the availability of training data for Machine Learning systems. For croplands to be identified and assessed, data needs to contain information beyond the type of cover and land use. The data needs to be able to provide information on the type of crops and should be ground referenced.

This is important because a Machine Learning model is only as useful as the quality of the training data used to create the model. The better the quality of the representative data, the higher is its ability to identify and assess anomalies and patterns. When the training datasets are of high quality, the algorithms of Machine Learning systems work more effectively. 

Data that is globally representative also helps Machine Learning systems since it can help run the model on a larger scale. There are still many gaps between the true data sets that are analyzed by Machine Learning systems and the training data that is available for systems. It is imperative to fill this gap before we can expect higher quality, more accurate outputs, and analysis from Machine Learning systems.

One of the ways to improve the training data and fill this gap is to make local training data sets more easily available and create a common arsenal for this data to be used by stakeholders. 

The second challenge that comes with Earth Observation and Machine Learning in agriculture is the georeferencing of data. For example, large amounts of data from a field and its neighbouring regions are required to analyze crop yield patterns in an area. The Earth Observation system collects a large sample of estimated yields. 

If this data is georeferenced, it can be converted into training datasets for Machine Learning systems with low effort. The opportunities that open up simply by georeferencing Earth Observation data are endless. One of the ways to make higher quality georeferenced data available is to streamline the collection of data. 

Eventually, it will come down to technologies and the stakeholders working together as a collective global effort to bring about the change we so desperately need to see in agriculture and land management at a global scale.

How is Artificial Intelligence, Machine learning and Deep Learning helping healthcare