Early crop yield prediction can address one of the most important agricultural challenges—feeding a growing population. The results of this prediction can be used to maximize crop yields. Crop growth is determined by a variety of weather, soil, and other micro-climatic variables. In addition, one of the most important parameters affecting crop growth, and over which the farmer has a large influence, is the selection of a sowing date. There are a few different models and simulators for predicting crop yields. However, they are rarely user-friendly. This paper focuses on developing a user-friendly mobile client that uses a simulator in the background to determine the sowing date which results in the highest crop yield. In the mobile application, a user selects the fixed interval of sowing dates and a crop type to obtain the sowing date that gives the highest predicted yield.
- APSIM next generation
- Crop yield prediction
- Weather data
- Mobile client
- Sowing date
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Commonwealth Scientific and Industrial Research Organisation, https://www.csiro.au/en/.
Machine learning for large-scale crop yield forecasting. Agri. Syst. 187, 103016 (2021)
Maresma, A., Ballesta, A., Santiveri, F., Lloveras, J.: Sowing date affects maize development and yield in irrigated mediterranean environments. Agriculture 9(3) (2019)
Long, N.V., Assefa, Y., Schwalbert, R., Ciampitti, I.A.: Maize yield and planting date relationship: a synthesis-analysis for us high-yielding contest-winner and field research data. Front. Plant Sci. 8, 2106 (2017)
Baum, M.E., Archontoulis, S.V., Licht, M.A.: Planting date, hybrid maturity, and weather effects on maize yield and crop stage. Agron. J. 111(1), 303–313
Dahmardeh, M.: Effects of sowing date on the growth and yield of maize cultivars (zea mays l.) and the growth temperature requirements. vol. 11 (2012)
Salo, T.J., Palosuo, T., Kersebaum, K.C., Nendel, C., Angulo, C., Ewert, F., Bindi, M., Calanca, P., Klein, T., Moriondo, M., et al.: Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization. J. Agric. Sci. 154(7), 1218–1240 (2016)
Buhiniček, I., Kaučić, D., Kozić, Z., Jukić, M., Gunjača, J., Šarčević, H., Stepinac, D., Šimić, D.: Trends in maize grain yields across five maturity groups in a long-term experiment with changing genotypes. Agriculture 11(9) (2021)
Saccomani, P.: People spent 90% of their mobile time using apps in 2021 (2021). https://www.mobiloud.com/blog/mobile-apps-vs-the-mobile-web
This work has been supported by the project IoT-field: An Ecosystem of Networked Devices and Services for IoT Solutions Applied in Agriculture funded by European Union from the European Regional Development Fund.
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Kralj, I., Skocir, P., Jezic, G. (2022). Mobile Client for Crop Yield Prediction Based on Weather Data. In: Jezic, G., Chen-Burger, YH.J., Kusek, M., Šperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2022. Smart Innovation, Systems and Technologies, vol 306. Springer, Singapore. https://doi.org/10.1007/978-981-19-3359-2_4
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3358-5
Online ISBN: 978-981-19-3359-2