Artificial Intelligence in Agriculture and Weather Forecasting
The world is facing a number of converging climate change challenges: population growth, more frequent extreme weather events, and a need for the sustainable production of nutritious food. Some say that machine learning can support us to mitigate and prepare for such consequences of climate change, however, it is not a silver bullet. In this interview, Congcong Sun and Chiem van Straaten discuss the challenges of machine learning in agriculture and weather forecasting, and the similarities and differences between their respective fields.
On November 16th, 2022, ICAI organizes the ‘ICAI Day: Artificial Intelligence and Climate Change’ where Congcong, Chiem, and many other researchers will talk about how AI can be used to mitigate and prepare for the consequences of climate change. Want to join? Sign up!
 | Congcong Sun is an assistant professor in learning-based Control at Wageningen University & Research (WUR) and Lab Manager of the ICAI AI for Agro-Food Lab. Her research interests are in using learning-based control to explore the overlap between machine learning and automatic control and apply them to agricultural production. |
 | Chiem van Straaten is a PhD student at the Vrije Universiteit Amsterdam (VU) and the Royal Netherlands Meteorological Institute (KNMI). His research focuses on improving sub-seasonal probabilistic forecasts of European high-impact weather events using machine-learning techniques. |
Congcong and Chiem, could you tell me what your research is about, and how it is connected to artificial intelligence?
Congcong: Yes, of course. My research focus is on learning-based autonomous control in agricultural production. For instance, in a greenhouse or vertical farm, climate control can be optimized to make the crops grow under more favorable conditions and produce a better quality crop. Another example is logistical planning for agro workers, such as harvesting robots in a multi-agent setting. Learning-based control applications are complex, which is why I mainly use deep reinforcement learning, which is the combination of reinforcement learning algorithms with neural networks.
Chiem: The research that I conduct pertains to studying and making predictions about weather and climate extremes. Many industries, such as agriculture production, depend on accurate weather forecasting. Understanding our climate better is crucial for preparing ourselves for extreme weather and at the same time allows industries to use their resources more efficiently. However, predicting weather events far in advance is extremely tough due to time lags, the conditional nature of observed patterns, and the multitude of factors influencing one another. Machine learning has the potential to deal with such levels of complexity, which is why I am interested in applying it to weather forecasting.
Do you see any similarities or differences between your research?
Congcong: I believe our research is interconnected. As Chiem mentioned, weather patterns are a large source of uncertainty within the agricultural industry, particularly for those applications where the farm is located in an uncontrolled environment, such as open-air farms.
In agriculture, the weather is not the only source of uncertainty, however. Uncertainty arises from the crops themselves. Different crops have optimal growing conditions, which means that a control policy that is effective for one crop might not be effective for another. Even if you were to place a different crop in the exact same greenhouse environment, you would need a vastly different policy for controlling it. What are your thoughts on that, Chiem?
Chiem: Yes, you are trying to tackle something that inherently is multivariate, which is similar to weather forecasting. Although I am not well-versed in the specifics of agriculture, I can imagine that you need to take into account many factors such as irrigation, lighting, and temperature?
Congcong: Yes, indeed. When we seek to regulate the climate within a greenhouse, there are a lot of variables we need to consider, like humidity, irrigation, fertilization, light, and temperature. Analyzing the relationships between these variables requires knowledge from various disciplines such as plant physiology and biology. Additionally, certain relationships might not have been discovered yet, which adds to the complexity of balancing these variables. The combination of machine learning and automatic control can help us explore some of these relationships and translate them into knowledge about how to best regulate these environments.
Chiem: Ah, exactly. Here, I see a great similarity between autonomous control of agriculture environments and the prediction of weather patterns. For a long time, physical numerical prediction models have been developed in order to incorporate as many of the processes that are known to be important for weather prediction as possible. However, it is also known that these models are not perfect, as the weather is extremely complex. Therefore, we attempt to replace parts of the numerical models with statistical models to capture yet-to-be-discovered processes
Congcong: Yes, indeed. What kind of data do you use to make weather forecasts?
Chiem: In the non-statistical forecasting models specifically, we use a plethora of data to make weather forecasts, including humidity, pressure, air temperature, and wind speed. Like the input, the output is often multivariate, similar to learning-based agriculture control. Another similarity might be that in both domains you encounter challenges due to cycles. For instance, I could imagine that in agriculture you need to take the growing cycle of plants into account, which is different for every plant. In weather forecasting, you also have to deal with many different cycles at the same time, such as the seasonal cycle, weekly cycles, and daily cycles.
Congcong: Yes, exactly! Plants have different optimal growing cycles. In greenhouses with multiple plants, it could be that different growing cycles overlap similarly to how cycles overlap in weather forecasting. It is interesting to see so many similarities between our two domains!
In your conversation, you mentioned some applications of machine learning in your respective domains. One challenge we often hear about is related to trustworthiness, especially in applications with high degrees of uncertainty. Are companies in your industry enthusiastic or reluctant to work with machine learning?
Congcong: Greenhouse climate control is quite mature in the Netherlands. Some commercial greenhouses have already implemented automated control, however, we are still not making use of all available cutting-edge sensing techniques. The adoption of such techniques by farmers might be slow since they are expensive and if they do not work as intended, it could ruin a farmer’s business. Also, farmers might be hesitant to trust machine learning technology, since it is a relatively new technology.
Chiem: As Congcong noted; the trustworthiness of a system is crucial for its widespread acceptance. Applications such as heatwave prediction are not quite ready for widespread use because heat waves have to be predicted far in advance, which is immensely tough to do accurately. Short-term forecasting applications, such as rainfall forecasting applications have a track record of successful predictions, however. Moreover, weather forecasting has rapid update cycles, so if you make an errant forecast today, you still have a chance tomorrow to forecast the same thing, but do so with greater accuracy. For heatwave prediction, such errant predictions have way more severe consequences. In agriculture, I could imagine the consequences are similarly more severe. What do you think Congcong?
Congcong: I agree with you Chiem. Plants are quite sensitive, so if a wrong prediction leads to hazardous conditions in which the plants cannot survive for long, the grower might lose all of their plants. While system control in agriculture does not come with direct harm to humans, like in autonomous driving, the margins on crops are small. Therefore, they are in general more averse to using machine learning and statistical modeling approaches in general.
Chiem, during the ICAI Day, a day revolving around the numerous challenges regarding machine learning and climate change, you will walk us through a heat wave prediction use case. What would you say the largest hurdle is in this research?
Chiem: The primary challenge in climate change research is the interaction between processes across different scales. On a local scale, processes such as heat exacerbation due to dry soil conditions or particular local atmospheric configurations can influence heat waves. However, such local conditions can also be synchronized across the scale of the complete northern hemisphere, which means that hundreds of kilometers away, very specific conditions might also be an indication of an impending heatwave. This can become increasingly more complex when you, for instance, include global connections.
The interaction across these many scales creates challenges in determining the resolution of the data you need and also what algorithm is most suitable to use. Additionally, climate change is actively changing our data distributions as we speak. Data that we gathered in the past might therefore have different weather dynamics than the weather right now, which makes generalizing very difficult. To an extent, your machine learning model is always extrapolating.
That is intriguing, thank you for your explanation! Congcong, during the ICAI Day you will moderate a lunch table discussion on Artificial Intelligence and Agriculture. What do you plan to discuss and why should people join?
Congcong: During the lunch table discussion, I would like to come together and talk about the current challenges of applying AI to agriculture; the popular and potential AI solutions to confront these challenges; as well as the future trends of applying AI to Agriculture. I believe it is valuable to join since it will be a very good chance for researchers, engineers, and students who are working in this area, or even just feel interested in this area, to ask their questions, share their opinions, and also may get some answers about their doubts through the discussions! Beyond that, it will also be a very good chance to build your network and explore potential collaborations for the future.
To round off; when would you say your research is a success?
Congcong: Any progress of my research, I consider a success and will make me happy. These are things such as my PhD students achieving a small step, solving pressing challenges for farmers, and making food production more sustainable by reducing emissions and energy use.
Chiem: One large success would be the ability to answer questions regarding climate change attribution such as: how much has climate change exacerbated the impact of this specific extreme weather event or made it more frequent? Being able to answer such questions confidently would allow us to hold parties, such as big emitters, accountable. While far off, I believe that machine learning has the potential to give us the tools necessary to do this in the future.
On November 16th, 2022, ICAI organizes the ‘ICAI Day: Artificial Intelligence and Climate Change’ where Congcong, Chiem, and many other researchers will talk about how AI can be used to mitigate and prepare for the consequences of climate change. Want to join? Sign up!