Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Sun, Chen; Liao, Feng; Zhang, Zhou; Ma, Yuchi; Crosby, Trevor W.; Naber, Mack R.; Wang, Yi

doi:10.3390/s20185293

Cited by 43 publications

(30 citation statements)

References 43 publications

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“…For the LiDAR data, the RF method provided slightly higher median accuracies than PLSR and SVR, with lower variability in R 2 values (more reliability). When VNIR data was the only input, PLSR yielded more accurate results, which is similar to the results obtained in [10] yield prediction of potatoes using VNIR hyperspectral data. For all other data sources, SVR yielded a higher median R 2 .…”

Section: Discussionsupporting

confidence: 82%

“…However, traditional methods of biomass measurement involving labor-intensive and time-consuming destructive sampling do not meet the requirements for timely evaluation of the genotypes in large-scale breeding programs. Recently, remote sensing (RS) data have been explored for estimation of many phenotypic traits, 2 of 35 including leaf area index (LAI) [2,3], canopy height [4,5], nitrogen content [6], and biomass [7][8][9][10][11], to replace traditional in-field phenotyping.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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“…The machine learning models combining optimal VIs and cultivar information [53] showed good potential in yield estimation, which is consistent with several previous studies [6,11,28]. SVM and RFR are two popular machine learning models used in precision agriculture and remote sensing data analysis, and one was found to perform better than the other in different studies [6,19,31]. In this study, although the RFR performed better than the SVR for the calibration process of a larger dataset, they performed similarly for a smaller validation dataset.…”

Section: Discussionsupporting

confidence: 87%

“…Potato yield is typically associated with seed quality as well as site-specific interactions between cultivars and environment conditions. The effects of soil type [16], nutrients [17,18], crop management practices [19,20], cultivar [21,22], seed quality [1,23], and weather conditions [24][25][26] on potato production have been explored extensively. Recent studies that incorporated multi-source data in yield prediction and nitrogen (N) recommendations have achieved better results than studies based on remote sensing data alone.…”

Section: Introductionmentioning

confidence: 99%

Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

Miao

Rosen

et al. 2021

Remote Sensing

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Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

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“…Ref. [32] utilized UAVs to gather hyperspectral data of potato tuber growth at the resolution of 2.5 cm/px. They utilized traditional ML methods, such as linear models and decision trees, to perform tuber yield estimation using individual data points gathered in-season at the intra-field scale, achieving 0.63 R 2 -score for the tuber yield prediction accuracy with a Ridge regression.…”

Section: Discussionmentioning

confidence: 99%

Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

Nevavuori¹,

Narra

Linna

et al. 2020

Remote Sensing

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Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications. At the same time, the increasing availability of yield monitoring devices in harvesters enables input-target mapping of in-season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems. Using time series UAV RGB and weather data collected from nine crop fields in Pori, Finland, we evaluated the feasibility of spatio-temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data. Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures with full 15 week image frame sequences from the whole growing season of 2018. The best performing architecture, the 3D-CNN, was then evaluated with several shorter frame sequence configurations from the beginning of the season. With 3D-CNN, we were able to achieve 218.9 kg/ha mean absolute error (MAE) and 5.51% mean absolute percentage error (MAPE) performance with full length sequences. The best shorter length sequence performance with the same model was 292.8 kg/ha MAE and 7.17% MAPE with four weekly frames from the beginning of the season.

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Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

Cited by 43 publications

References 43 publications

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning

Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

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