The Application of Quantum Computing in Crop Modeling
Quantum computing is the future, and as each day passes, we are still discovering new and innovative ways to use this technology. One new place I believe that quantum computing will be a game-changer is in crop modeling.
As mentioned in the previous blog post (you can check it out here), crop modeling simulates the growth of crops based on environmental characteristics, crop management, and crop traits. However, crop modeling is still not perfect, and the algorithms used for crop modeling oftentimes take time to perfect, but only be used for locations with similar factors as the one the model is based off of.
Another problem that currently exists within crop modeling is the scale that most models are currently at. Most models run on a field scale, meaning that they only take into factors like leaf size, or weather. However, crop modeling still can be improved.
Researchers believe that if crop modeling can be a multiscale model, then crop models’ accurate production and environmental impact abilities can be improved, helping to feed the world.
For crop modeling, a multiscale model would have the field scale, but also include other factors from a molecular and atomic scale, like plant genetics. In one recent study, Bin Peng et al., a University of Illinois at Urbana-Champaign postdoctoral researcher and co-author of the study, stated that, “Modeling at this scale is critical, but we would like to incorporate information from gene-to-cell and regional-to-global scale data into our modeling framework.”
And this is where I believe that quantum computing can be implemented the best!
A multiscale framework!
Quantum computing is known for its fast processing speed, being able to perform many times more computations than a classical system can in the same amount of time. Quantum computing can quickly optimize new crop models, so that the algorithm of a new crop model can quickly be perfected when modeling a location that is not similar to an area that has already been modeled. Soon Quantum computing will process these new multilevel frameworks that classical computers cannot handle, making the crop modeling process more efficient.
Quantum computing, optimizing new and multiscaled crop modeling algorithms, will help small scale producers increase their crop yields, and help feed the world.
References:
Bin Peng, Kaiyu Guan, Jinyun Tang, Elizabeth A. Ainsworth, Senthold Asseng, Carl J. Bernacchi, Mark Cooper, Evan H. Delucia, Joshua W. Elliott, Frank Ewert, Robert F. Grant, David I Gustafson, Graeme L. Hammer, Zhenong Jin, James W. Jones, Hyungsuk Kimm, David M. Lawrence, Yan Li, Danica L. Lombardozzi, Amy Marshall-Colon, Carlos D. Messina, Donald R. Ort, James C. Schnable, C. Eduardo Vallejos, Alex Wu, Xinyou Yin, Wang Zhou. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 2020; 6 (4): 338 DOI: 10.1038/s41477–020–0625–3
T.Rajasekaran, P.Jayasheelan,P.Jayasheelan. “Predictive Analysis in Agriculture to Improve the Crop Productivity using ZeroR algorithm.” International Journal of Computer Science and Engineering Communications 4.2 (2016): 1397–1401.
University of Illinois at Urbana-Champaign, News Bureau. “Multiscale crop modeling effort required to assess climate change adaptation.” ScienceDaily. ScienceDaily, 14 May 2020. <www.sciencedaily.com/releases/2020/05/200514143558.htm>.
