County-level corn yield prediction using supervised machine learning
ABSTRACTThe main objectives of this study are (1) to compare several machine learning models to predict county-level corn yield in the study area and (2) to compare the feasibility of machine learning models for in-season yield prediction. We acquired remotely sensed vegetation indices data from mod...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2253985 |
| _version_ | 1797692476094414848 |
|---|---|
| author | Shahid Nawaz Khan Abid Nawaz Khan Aqil Tariq Linlin Lu Naeem Abbas Malik Muhammad Umair Wesam Atef Hatamleh Farah Hanna Zawaideh |
| author_facet | Shahid Nawaz Khan Abid Nawaz Khan Aqil Tariq Linlin Lu Naeem Abbas Malik Muhammad Umair Wesam Atef Hatamleh Farah Hanna Zawaideh |
| author_sort | Shahid Nawaz Khan |
| collection | DOAJ |
| description | ABSTRACTThe main objectives of this study are (1) to compare several machine learning models to predict county-level corn yield in the study area and (2) to compare the feasibility of machine learning models for in-season yield prediction. We acquired remotely sensed vegetation indices data from moderate resolution imaging spectroradiometer using the Google Earth Engine (GEE). Vegetation indices for a span of 15 years (2006–2020) were processed and downloaded using GEE for the months corresponding to crop growth (April–October). We compared nine machine learning models to predict county-level corn yield. Furthermore, we analyzed the in-season yield prediction performance using the top three machine learning models. The results show that partial least square regression (PLSR) outperformed other machine learning models for corn yield prediction by achieving the highest training and testing performance. The study area’s top three models for county-level corn yield prediction were PLSR, support vector regression (SVR) and ridge regression. For in-season yield prediction, the SVR model performed comparatively well by achieving testing R2 = 0.875. For in-season corn yield prediction, SVR outperformed other models. The results show that machine learning models can predict both in-season yield (best model R2 = 0.875) and end-of-season yield (best model R2 = 0.861) with satisfactory performance. The results indicate that remote sensing data and machine learning models can be used to predict crop yield before the harvest with decent performance. This can provide useful insights in terms of food security and early decision making related to climate change impacts on food security. |
| first_indexed | 2024-03-12T02:29:10Z |
| format | Article |
| id | doaj.art-963ae4a1a5fb4738b84af646e2e45dc5 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-03-12T02:29:10Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-963ae4a1a5fb4738b84af646e2e45dc52023-09-05T10:15:50ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542023-12-0156110.1080/22797254.2023.2253985County-level corn yield prediction using supervised machine learningShahid Nawaz Khan0Abid Nawaz Khan1Aqil Tariq2Linlin Lu3Naeem Abbas Malik4Muhammad Umair5Wesam Atef Hatamleh6Farah Hanna Zawaideh7Department of Geography, University of Alabama, Tuscaloosa, USAFaculty of Information Technology and Communication Sciences (Data Science), Tampere University, Tampere, FinlandDepartment of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi, USAKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaDepartment of Remote Sensing and GIS, PMAS Arid Agriculture University, Rawalpindi, PakistanDépartement de Géographie, Université de Montréal, Montréal, QC, CanadaDepartment of computer science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Business Intelligence and Data Analysis, Faculty of Financial and Business Science, Irbid National University, Irbid, JordanABSTRACTThe main objectives of this study are (1) to compare several machine learning models to predict county-level corn yield in the study area and (2) to compare the feasibility of machine learning models for in-season yield prediction. We acquired remotely sensed vegetation indices data from moderate resolution imaging spectroradiometer using the Google Earth Engine (GEE). Vegetation indices for a span of 15 years (2006–2020) were processed and downloaded using GEE for the months corresponding to crop growth (April–October). We compared nine machine learning models to predict county-level corn yield. Furthermore, we analyzed the in-season yield prediction performance using the top three machine learning models. The results show that partial least square regression (PLSR) outperformed other machine learning models for corn yield prediction by achieving the highest training and testing performance. The study area’s top three models for county-level corn yield prediction were PLSR, support vector regression (SVR) and ridge regression. For in-season yield prediction, the SVR model performed comparatively well by achieving testing R2 = 0.875. For in-season corn yield prediction, SVR outperformed other models. The results show that machine learning models can predict both in-season yield (best model R2 = 0.875) and end-of-season yield (best model R2 = 0.861) with satisfactory performance. The results indicate that remote sensing data and machine learning models can be used to predict crop yield before the harvest with decent performance. This can provide useful insights in terms of food security and early decision making related to climate change impacts on food security.https://www.tandfonline.com/doi/10.1080/22797254.2023.2253985Remote sensingyield predictionMODISvegetation indicesfood security |
| spellingShingle | Shahid Nawaz Khan Abid Nawaz Khan Aqil Tariq Linlin Lu Naeem Abbas Malik Muhammad Umair Wesam Atef Hatamleh Farah Hanna Zawaideh County-level corn yield prediction using supervised machine learning European Journal of Remote Sensing Remote sensing yield prediction MODIS vegetation indices food security |
| title | County-level corn yield prediction using supervised machine learning |
| title_full | County-level corn yield prediction using supervised machine learning |
| title_fullStr | County-level corn yield prediction using supervised machine learning |
| title_full_unstemmed | County-level corn yield prediction using supervised machine learning |
| title_short | County-level corn yield prediction using supervised machine learning |
| title_sort | county level corn yield prediction using supervised machine learning |
| topic | Remote sensing yield prediction MODIS vegetation indices food security |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2253985 |
| work_keys_str_mv | AT shahidnawazkhan countylevelcornyieldpredictionusingsupervisedmachinelearning AT abidnawazkhan countylevelcornyieldpredictionusingsupervisedmachinelearning AT aqiltariq countylevelcornyieldpredictionusingsupervisedmachinelearning AT linlinlu countylevelcornyieldpredictionusingsupervisedmachinelearning AT naeemabbasmalik countylevelcornyieldpredictionusingsupervisedmachinelearning AT muhammadumair countylevelcornyieldpredictionusingsupervisedmachinelearning AT wesamatefhatamleh countylevelcornyieldpredictionusingsupervisedmachinelearning AT farahhannazawaideh countylevelcornyieldpredictionusingsupervisedmachinelearning |