Review of Methods and Models for Potato Yield Prediction

Magdalena Piekutowska and Gniewko Niedbała ()
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Magdalena Piekutowska: Department of Botany and Nature Protection, Institute of Biology, Pomeranian University in Słupsk, 22b Arciszewskiego St., 76-200 Słupsk, Poland
Gniewko Niedbała: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland

Agriculture, 2025, vol. 15, issue 4, 1-31

Abstract: This article provides a comprehensive overview of the development and application of statistical methods, process-based models, machine learning, and deep learning techniques in potato yield forecasting. It emphasizes the importance of integrating diverse data sources, including meteorological, phenotypic, and remote sensing data. Advances in computer technology have enabled the creation of more sophisticated models, such as mixed, geostatistical, and Bayesian models. Special attention is given to deep learning techniques, particularly convolutional neural networks, which significantly enhance forecast accuracy by analyzing complex data patterns. The article also discusses the effectiveness of other algorithms, such as Random Forest and Support Vector Machines, in capturing nonlinear relationships affecting yields. According to standards adopted in agricultural research, the Mean Absolute Percentage Error (MAPE) in the implementation of prediction issues should generally not exceed 15%. Contemporary research indicates that, through the use of advanced and accurate algorithms, the value of this error can reach levels of even less than 10 per cent, significantly increasing the efficiency of yield forecasting. Key challenges in the field include climatic variability and difficulties in obtaining accurate data on soil properties and agronomic practices. Despite these challenges, technological advancements present new opportunities for more accurate forecasting. Future research should focus on leveraging Internet of Things (IoT) technology for real-time data collection and analyzing the impact of biological variables on yield. An interdisciplinary approach, integrating insights from ecology and meteorology, is recommended to develop innovative predictive models. The exploration of machine learning methods has the potential to advance knowledge in potato yield forecasting and support sustainable agricultural practices.

Keywords: potato; yield prediction; statistical models; process-based models; machine learning; deep learning; neural networks; model complexity; predictive accuracy (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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