Methodological evolution of potato yield prediction: a comprehensive review

Lin, Yongxin; Li, Shuang; Duan, Shaoguang; Ye, Yanran; Li, Bo; Li, Guangcun; Lyv, Dianqiu; Jin, Liping; Bian, Chunsong; Liu, Jiangang

doi:10.3389/fpls.2023.1214006

Cited by 15 publications

(1 citation statement)

References 150 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of satellite Earth observation systems, improved RS data availability for practical applications, and the increased quality of these data in terms of spatial and temporal resolution, satellite data have been increasingly incorporated into potato predictive models [15,16]. When it comes to potato yield forecasting at field scale, there are few publications describing the combined use of ground-based data (soil, agronomy, weather) and satellite data (vegetation indices) as the input parameters for models [12,[17][18][19][20][21]. In the practical application of predictive models in agricultural decision support systems, the flexibility of data source selection for modelling becomes an important functional requirement, considering the aforementioned data availability and quality issues.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

Kurek,

Niedbała,

Wojciechowski

et al. 2023

Agriculture

View full text Add to dashboard Cite

This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.

show abstract