Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia)
Information on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine le...
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2025-04-01
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| author | Gordan Mimić Amit Kumar Mishra Miljana Marković Branislav Živaljević Dejan Pavlović Oskar Marko |
| author_facet | Gordan Mimić Amit Kumar Mishra Miljana Marković Branislav Živaljević Dejan Pavlović Oskar Marko |
| author_sort | Gordan Mimić |
| collection | DOAJ |
| description | Information on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. Ground truth data were provided for the Vojvodina region (Serbia), an area with intensive agricultural production, considering winter wheat, maize and soybean fields with exact harvest dates, for the period 2017–2020, including 592 samples in total. Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical–horizontal (VH) and vertical–vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. Clustering of harvested and unharvested fields was performed with Principal Component Analysis, multidimensional scaling and t-distributed Stochastic Neighbour Embedding, for initial cluster visualization. It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an <i>R</i><sup>2</sup> score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together. |
| format | Article |
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| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-1d3cb341dc45440f8dda884a4b4cfb922025-08-20T03:08:59ZengMDPI AGSensors1424-82202025-04-01257223910.3390/s25072239Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia)Gordan Mimić0Amit Kumar Mishra1Miljana Marković2Branislav Živaljević3Dejan Pavlović4Oskar Marko5BioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaNational Spectrum Centre, Aberystwyth University, Penglais, Aberystwyth SY23 2JH, UKBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaBioSense Institute, University of Novi Sad, 21000 Novi Sad, SerbiaInformation on the harvest date of crops can help with logistics management in the agricultural industry, planning machinery operations and also with yield prediction modelling. In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. Ground truth data were provided for the Vojvodina region (Serbia), an area with intensive agricultural production, considering winter wheat, maize and soybean fields with exact harvest dates, for the period 2017–2020, including 592 samples in total. Data from the Sentinel-1 satellite were used in the study. Time series of backscattering coefficients for vertical–horizontal (VH) and vertical–vertical (VV) polarisations, both from ascending and descending orbits, were collected from Google Earth Engine. Clustering of harvested and unharvested fields was performed with Principal Component Analysis, multidimensional scaling and t-distributed Stochastic Neighbour Embedding, for initial cluster visualization. It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. Finally, regression models were developed for the prediction of the harvest date using Random Forest and the long short-term memory network, with the latter achieving better results: an <i>R</i><sup>2</sup> score of 0.72, mean absolute error of 6.80 days and root mean squared error of 9.25 days, for all crops considered together.https://www.mdpi.com/1424-8220/25/7/2239SARSentinel-1Google Earth Enginemachine learningharvest datesagricultural production |
| spellingShingle | Gordan Mimić Amit Kumar Mishra Miljana Marković Branislav Živaljević Dejan Pavlović Oskar Marko Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) Sensors SAR Sentinel-1 Google Earth Engine machine learning harvest dates agricultural production |
| title | Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) |
| title_full | Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) |
| title_fullStr | Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) |
| title_full_unstemmed | Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) |
| title_short | Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) |
| title_sort | machine learning based harvest date detection and prediction using sar data for the vojvodina region serbia |
| topic | SAR Sentinel-1 Google Earth Engine machine learning harvest dates agricultural production |
| url | https://www.mdpi.com/1424-8220/25/7/2239 |
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