Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images

Liu, Yang; Feng, Haikuan; Yue, Jibo; Li, Zhenhai; Yang, Guijun; Song, Xiaoyu; Yang, Xiaodong; Zhao, Yu

doi:10.1016/j.compag.2022.107089

Cited by 81 publications

(39 citation statements)

References 38 publications

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“…Potato seed tubers were sown on 28 March 2019 and harvested on 9 July 2019. The experimental area was divided into the density experimental area (T plots), N experimental area (N plots) and potassium fertilizer experimental area (K plots) to increase the spatial difference of potato growth ( Liu et al., 2022 ). Among them, three levels were established in the density test area, including 60,000 plants/hm 2 (T0), 72,000 plants/hm 2 (T1), and 84,000 plants/hm 2 (T2).…”

Section: Experiments and Methodsmentioning

confidence: 99%

“…Staple crops, such as rice ( Oryza sativa ), maize ( Zea mays ), and wheat ( Triticum aestivum ), have a limited scope for increasing yields and high requirements for irrigation systems, which results in high production costs and low potential. Alternatively, potato ( Solanum tuberosum ) is becoming increasingly important in ensuring global food security as the fourth largest food crop with a short growth cycle and the ability to adapt to the environment ( Liu et al., 2022 ). In recent years, the excessive application of nitrogen (N) fertilizer to aggressively maximize potato yields in some regions has reduced the efficiency of N fertilizer use, resulting in increased production costs and wasted resources and triggering potential environmental risks ( Nayak et al., 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Fan

Feng²,

Jin³

et al. 2022

Front. Plant Sci.

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Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (H), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato H and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: R2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R2 = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.

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Section: Experiments and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Fan

Feng²,

Jin³

et al. 2022

Front. Plant Sci.

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“…Variation in potato CH can provide researchers with important information on crop health, growth, and response to environmental impacts. Therefore, CH plays a decisive role in monitoring crop growth [56][57][58][59][60], so CH data can not only guide agricultural production management but also provide a reliable theoretical basis for plant morphology research and crop selection and breeding. In this study, the CH information is extracted without generating a digital elevation model of bare-soil points by kriging interpolation The use of GPR fed with spectral information combined with CH improved the R 2 value and reduced the RMSE and NRMSE values, indicating that this method provides the most accurate estimate of potato AGB.…”

Section: Monitoring Potato Crop Heightmentioning

confidence: 99%

“…Therefore, CH plays a decisive role in monitoring crop growth [56][57][58][59], so CH data can not only guide agricultural production management but also provide a reliable theoretical basis for plant morphology research and crop selection and breeding. In this study, the CH information is extracted without generating a digital elevation model of bare-soil points by kriging interpolation because potatoes are planted in monopolies that reach a certain height, so ignoring their height and taking the difference would cause large extraction errors [60].…”

Section: Monitoring Potato Crop Heightmentioning

confidence: 99%

“…Based on the optimal VIs, AGB estimation models were constructed to monitor the growth of rice [8], maize [11], and winter wheat [34]. Winter wheat AGB was accurately estimated by combining different VIs with partial-least-squares regression (PLSR) [35], stepwise regression (SWR) [36], and multiple linear regression (MLR) [23], respectively. Other studies estimate AGB by applying several regression techniques simultaneously, using correlation analysis to confirm sensitive spectral parameters, and then building an AGB estimation model through PLSR and MLR.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression

Liu

Feng²,

Yue³

et al. 2022

Remote Sensing

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Above-ground biomass (AGB) is an important indicator for monitoring crop growth and plays a vital role in guiding agricultural management, so it must be determined rapidly and nondestructively. The present study investigated the extraction from UAV hyperspectral images of multiple variables, including canopy original spectra (COS), first-derivative spectra (FDS), vegetation indices (VIs), and crop height (CH) to estimate the potato AGB via the machine-learning methods of support vector machine (SVM), random forest (RF), and Gaussian process regression (GPR). High-density point clouds were combined with three-dimensional spatial information from ground control points by using structures from motion technology to generate a digital surface model (DSM) of the test field, following which CH was extracted based on the DSM. Feature bands in sensitive spectral regions of COS and FDS were automatically identified by using a Gaussian process regression-band analysis tool that analyzed the correlation of the COS and FDS with the AGB in each growth period. In addition, the 16 Vis were separately analyzed for correlation with the AGB of each growth period to identify highly correlated Vis and excluded highly autocorrelated variables. The three machine-learning methods were used to estimate the potato AGB at each growth period and their results were compared separately based on the COS, FDS, VIs, and combinations thereof with CH. The results showed that (i) the correlations of COS, FDS, and VIs with AGB all gradually improved when going from the tuber-formation stage to the tuber-growth stage and thereafter deteriorated. The VIs were most strongly correlated with the AGB, followed by FDS, and then by COS. (ii) The CH extracted from the DSM was consistent with the measured CH. (iii) For each growth stage, the accuracy of the AGB estimates produced by a given machine-learning method depended on the combination of model variables used (VIs, FDS, COS, and CH). (iv) For any given set of model variables, GPR produced the best AGB estimates in each growth period, followed by RF, and finally by SVM. (v) The most accurate AGB estimate was achieved in the tuber-growth stage and was produced by combining spectral information and CH and applying the GPR method. The results of this study thus reveal that UAV hyperspectral images can be used to extract CH and crop-canopy spectral information, which can be used with GPR to accurately estimate potato AGB and thereby accurately monitor crop growth.

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Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

Merchan,

Calderon,

Broncano

et al. 2024

Lecture Notes in Geoinformation and Cartography

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Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images

Cited by 81 publications

References 38 publications

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression

Assessing Maize (Zea Mays) Biomass at Different Growth Stages: A UAV-Based Multispectral Study on Nitrogen Effects

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