Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data

被引:2
|
作者
Bogdanovski, Oliver Persson [1 ]
Svenningsson, Christoffer [1 ]
Mansson, Simon [2 ]
Oxenstierna, Andreas [3 ]
Sopasakis, Alexandros [1 ]
机构
[1] Lund Univ, Fac Sci, Dept Math, S-22100 Lund, Sweden
[2] Niftitech AB, Hedvig Mollers gata 12, S-22355 Lund, Sweden
[3] T Kartor AB, Olof Mohlins vag 12, S-29162 Kristianstad, Sweden
来源
AGRICULTURE-BASEL | 2023年 / 13卷 / 04期
基金
瑞典研究理事会;
关键词
precision agriculture; Sentinel-1; SAR; machine learning; yield prediction; despeckling; GROWTH;
D O I
10.3390/agriculture13040813
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography and local weather over four different years. We note that all of the input data were freely available. During training, we used information on winter wheat production over the fields of interest which was available from participating farmers. The two machine learning models we trained are the Light Gradient-Boosting Machine and a Feed-forward Neural Network. Our results show that Sentinel-1 data contain valuable information which can be used for training to predict winter wheat yield once two important steps are taken: performing a critical transformation of each pixel in the images to align it to the training data and then following up with despeckling treatment. Using this approach, we were able to achieve a top root mean square error of 0.75 tons per hectare and a top accuracy of 86% using a k-fold method with k=5. More importantly, however, we established that Sentinel-1 data alone are sufficient to predict yield with an average root mean square error of 0.89 tons per hectare, making this method feasible to employ worldwide.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] STRATIFIED MACHINE LEARNING MODELS FOR WHEAT YIELD ESTIMATION USING REMOTE SENSING DATA
    Khechba, Keltoum
    Belgiu, Mariana
    Laamrani, Ahmed
    Dong, Qi
    Stein, Alfred
    Chehbouni, Abdelghani
    IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024, 2024, : 1946 - 1949
  • [32] Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones
    Li, Zongpeng
    Chen, Zhen
    Cheng, Qian
    Fei, Shuaipeng
    Zhou, Xinguo
    DRONES, 2023, 7 (08)
  • [33] Genomic Prediction of Wheat Grain Yield Using Machine Learning
    Sirsat, Manisha Sanjay
    Oblessuc, Paula Rodrigues
    Ramiro, Ricardo S.
    AGRICULTURE-BASEL, 2022, 12 (09):
  • [34] Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning
    Mashaba-Munghemezulu, Zinhle
    Chirima, George Johannes
    Munghemezulu, Cilence
    SUSTAINABILITY, 2021, 13 (09)
  • [35] Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data
    Jeppesen, Jacob Hoxbroe
    Jacobsen, Rune Hylsberg
    Jorgensen, Rasmus Nyholm
    2020 23RD EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD 2020), 2020, : 557 - 564
  • [36] Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation
    Pan, Haizhu
    Chen, Zhongxin
    de Wit, Allard
    Ren, Jianqiang
    SENSORS, 2019, 19 (14)
  • [37] WINTER WHEAT YIELD ASSESSMENT USING LANDSAT 8 AND SENTINEL-2 DATA
    Skakun, S.
    Franch, B.
    Vermote, E.
    Roger, J. -C.
    Justice, C.
    Masek, J.
    Murphy, E.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 5964 - 5967
  • [38] Statistical and machine learning models for location-specific crop yield prediction using weather indices
    Ajith, S.
    Debnath, Manoj Kanti
    Karthik, R.
    INTERNATIONAL JOURNAL OF BIOMETEOROLOGY, 2024, 68 (12) : 2453 - 2475
  • [39] Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles
    Caballero, Gabriel
    Pezzola, Alejandro
    Winschel, Cristina
    Casella, Alejandra
    Angonova, Paolo Sanchez
    Orden, Luciano
    Berger, Katja
    Verrelst, Jochem
    Delegido, Jesus
    REMOTE SENSING, 2022, 14 (22)
  • [40] Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion
    Ashfaq, Muhammad
    Khan, Imran
    Alzahrani, Abdulrahman
    Tariq, Muhammad Usman
    Khan, Humera
    Ghani, Anwar
    IEEE ACCESS, 2024, 12 : 40947 - 40961