Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning
Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (...
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/18/5293 |
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| author | Chen Sun Luwei Feng Zhou Zhang Yuchi Ma Trevor Crosby Mack Naber Yi Wang |
| author_facet | Chen Sun Luwei Feng Zhou Zhang Yuchi Ma Trevor Crosby Mack Naber Yi Wang |
| author_sort | Chen Sun |
| collection | DOAJ |
| description | Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R<sup>2</sup> = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R<sup>2</sup> = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices. |
| first_indexed | 2024-03-10T16:18:07Z |
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| id | doaj.art-ef54f2b25be140adb34beb5dda0302bf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:18:07Z |
| publishDate | 2020-09-01 |
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| series | Sensors |
| spelling | doaj.art-ef54f2b25be140adb34beb5dda0302bf2023-11-20T13:55:52ZengMDPI AGSensors1424-82202020-09-012018529310.3390/s20185293Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine LearningChen Sun0Luwei Feng1Zhou Zhang2Yuchi Ma3Trevor Crosby4Mack Naber5Yi Wang6Biological Systems Engineering, University of Wisconsin–Madison, Madison, WI 53706, USABiological Systems Engineering, University of Wisconsin–Madison, Madison, WI 53706, USABiological Systems Engineering, University of Wisconsin–Madison, Madison, WI 53706, USABiological Systems Engineering, University of Wisconsin–Madison, Madison, WI 53706, USAHorticulture, University of Wisconsin-Madison, Madison, WI 53706, USAHorticulture, University of Wisconsin-Madison, Madison, WI 53706, USAHorticulture, University of Wisconsin-Madison, Madison, WI 53706, USAPotato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R<sup>2</sup> = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R<sup>2</sup> = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.https://www.mdpi.com/1424-8220/20/18/5293hyperspectral imagingmachine learningtuber yieldtuber setunmanned aerial vehicles |
| spellingShingle | Chen Sun Luwei Feng Zhou Zhang Yuchi Ma Trevor Crosby Mack Naber Yi Wang Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning Sensors hyperspectral imaging machine learning tuber yield tuber set unmanned aerial vehicles |
| title | Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning |
| title_full | Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning |
| title_fullStr | Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning |
| title_full_unstemmed | Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning |
| title_short | Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning |
| title_sort | prediction of end of season tuber yield and tuber set in potatoes using in season uav based hyperspectral imagery and machine learning |
| topic | hyperspectral imaging machine learning tuber yield tuber set unmanned aerial vehicles |
| url | https://www.mdpi.com/1424-8220/20/18/5293 |
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