Intelligent Crop Production: Data-integrative, Multi-task Learning Meets Crop Simulator

Project Details


Many studies have reported that global crop production needs to double by 2050 due to rising population, and boosting crop production is essential. This project focuses on computational approaches for this problem by using data about yields obtained under different conditions along with soil and weather data and drone and satellite images. Computational approaches can be roughly divided into two: 1) machine learning (ML), where a model is learnt from the data, and 2) simulators, where the data is generated from a model. Our contribution is two-fold: 1) we develop a new ML method, and 2) we combine ML with simulation. The first objective is to attain a high predictive performance for crop production. The second objective is to improve simulation quality with ML, which allows using new types of data, say satellite images, which cannot be used by conventional simulators. The project will develop state-of-the-art ML technology that will increase the crop production.
Short titleAiCropPro/Mamitsuka
Effective start/end date01/01/201831/12/2022

Collaborative partners

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Contextualized Graph Embeddings for Adverse Drug Event Detection

    Gao, Y., Ji, S., Zhang, T., Tiwari, P. & Marttinen, P., 17 Mar 2023, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings: European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II. Amini, M-R., Canu, S., Fischer, A., Guns, T., Kralj Novak, P. & Tsoumakas, G. (eds.). Springer, p. 605–620 16 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Open Access
    1 Citation (Scopus)
    24 Downloads (Pure)
  • Deformation equivariant cross-modality image synthesis with paired non-aligned training data

    Honkamaa, J., Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., Ruusuvuori, P. & Marttinen, P., Dec 2023, In: Medical Image Analysis. 90, p. 1-13 13 p., 102940.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    3 Downloads (Pure)
  • Proposal and extensive test of a calibration protocol for crop phenology models

    Wallach, D., Palosuo, T., Thorburn, P., Mielenz, H., Buis, S., Hochman, Z., Gourdain, E., Andrianasolo, F., Dumont, B., Ferrise, R., Gaiser, T., Garcia, C., Gayler, S., Harrison, M., Hiremath, S., Horan, H., Hoogenboom, G., Jansson, P. E., Jing, Q., Justes, E., & 20 othersKersebaum, K. C., Launay, M., Lewan, E., Liu, K., Mequanint, F., Moriondo, M., Nendel, C., Padovan, G., Qian, B., Schütze, N., Seserman, D. M., Shelia, V., Souissi, A., Specka, X., Srivastava, A. K., Trombi, G., Weber, T. K. D., Weihermüller, L., Wöhling, T. & Seidel, S. J., Aug 2023, In: Agronomy for Sustainable Development. 43, 4, 46.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    1 Citation (Scopus)
    7 Downloads (Pure)