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] Sweet corn yield prediction using machine learning models and field-level data
    Daljeet S. Dhaliwal
    Martin M. Williams
    Precision Agriculture, 2024, 25 : 51 - 64
  • [32] Sweet corn yield prediction using machine learning models and field-level data
    Dhaliwal, Daljeet S.
    Williams, Martin M.
    PRECISION AGRICULTURE, 2024, 25 (01) : 51 - 64
  • [33] MAPPING RICE AREA USING SENTINEL-1 SAR DATA AND DEEP LEARNING
    Shen, Guozhuang
    Nie, Chenwei
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3402 - 3405
  • [34] Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach
    Pelta, Ran
    Beeri, Ofer
    Tarshish, Rom
    Shilo, Tal
    REMOTE SENSING, 2022, 14 (11)
  • [35] Estimation of Surface Moisture Content using Sentinel-1 C-band SAR Data Through Machine Learning Models
    Datta, Subhadip
    Das, Pulakesh
    Dutta, Dipanwita
    Giri, Rakesh Kr.
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2021, 49 (04) : 887 - 896
  • [36] Estimation of Surface Moisture Content using Sentinel-1 C-band SAR Data Through Machine Learning Models
    Subhadip Datta
    Pulakesh Das
    Dipanwita Dutta
    Rakesh Kr. Giri
    Journal of the Indian Society of Remote Sensing, 2021, 49 : 887 - 896
  • [37] Soil Salinity Mapping of Plowed Agriculture Lands Combining Radar Sentinel-1 and Optical Sentinel-2 with Topographic Data in Machine Learning Models
    Tola, Diego
    Satge, Frederic
    Pillco Zola, Ramiro
    Sainz, Humberto
    Condori, Bruno
    Miranda, Roberto
    Yujra, Elizabeth
    Molina-Carpio, Jorge
    Hostache, Renaud
    Espinoza-Villar, Raul
    REMOTE SENSING, 2024, 16 (18)
  • [38] 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)
  • [39] Genomic Prediction of Wheat Grain Yield Using Machine Learning
    Sirsat, Manisha Sanjay
    Oblessuc, Paula Rodrigues
    Ramiro, Ricardo S.
    AGRICULTURE-BASEL, 2022, 12 (09):
  • [40] Wheat Crop Field and Yield Prediction using Remote Sensing and Machine Learning
    Ayub, Maheen
    Khan, Najeed Ahmed
    Haider, Rana Zeeshan
    PROCEEDINGS OF 2ND IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (ICAI 2022), 2022, : 158 - 164