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Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Pham, Hoa Thi; Awange, Joseph 1; Kuhn, Michael
1 Geodätisches Institut (GIK), Karlsruher Institut für Technologie (KIT)

Abstract:

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000150911
Veröffentlicht am 23.09.2022
Originalveröffentlichung
DOI: 10.3390/s22176609
Scopus
Zitationen: 12
Web of Science
Zitationen: 5
Dimensions
Zitationen: 11
Cover der Publikation
Zugehörige Institution(en) am KIT Geodätisches Institut (GIK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000150911
Erschienen in Sensors
Verlag MDPI
Band 22
Heft 17
Seiten Art.Nr. 6609
Vorab online veröffentlicht am 01.09.2022
Nachgewiesen in Dimensions
Scopus
Web of Science
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