Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Luo, Shanjun; He, Yingbin; Li, Qian; Jiao, Weihua; Zhu, Yu-Xian; Zhao, Xihai

doi:10.1186/s13007-020-00693-3

Cited by 28 publications

(18 citation statements)

References 60 publications

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“…To train the generated LUT database, a variety of ML algorithms have been introduced into hybrid methods for retrieving canopy traits. Among ML algorithms, Random Forest Regression (RF) and Gaussian Process Regression (GPR) have been well applied in several studies, due to their robustness and efficient implementation [39,[43][44][45][46][47][48][49]. RF is a regression tree-based ensemble algorithm which can handle several input variables without overfitting while also being less sensitive to outliers and noise [50,51].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

et al. 2021

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Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

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Section: Introductionmentioning

confidence: 99%

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

et al. 2021

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“…In addition, it is worth mentioning that not only climate change but also the potato variety, soil fertility, farming system, and production technology 5 , 21 can influence potato growth and development. Further research should consider possible climate change scenarios in the future, in combination with field conditions, irrigation technologies, and other modern measures, to provide a more comprehensive reference for potato cultivation management in Jilin Province.…”

Section: Discussionmentioning

confidence: 99%

“…Globally, potato ( Solanum tuberosum L.) is the fourth most widely cultivated crop after maize, rice, and wheat 19 , with more than 91.9 million tons produced annually across an area of about 4,789.5 thousand hectares, with average yield of 19.1 t ha -1 in China 20 . In 2015, China launched the potato staple food strategy, which acknowledged and facilitated the role of potatoes in maintaining food security 21 , 22 . However, potato cultivation faces possible challenges due to ongoing anthropogenic climate change 23 – 28 , which is impacting many agricultural systems 29 – 31 .…”

Section: Introductionmentioning

confidence: 99%

Impacts of climate change on suitability zonation for potato cultivation in Jilin Province, Northeast China

Zhu

Luo

et al. 2021

Sci Rep

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Global climate change is causing notable shifts in the environmental suitability of the main regions involved in potato cultivation and has, thus, changed the production potential of potatoes. These shifts can be mapped at fine scales to better understand climate change within areas of potato cultivation and to find infrastructural- and breeding-based solutions. As a case study, we have identified and mapped the structural and spatial shifts that occurred in areas suitable for potato cultivation in Jilin Province, China. We identified a discontinuity in climate change trends between 1961 and 2018 based on data for Jilin Province, and analyzed the averages and linear trends for six important climatic parameters. We used the averages of these climatic parameters to establish climate models for the province and determined cultivation using a multi-criteria, decision-based model that integrates Analytical Hierarchy Process Weighted Principal Component Analysis (AHP-PCA) and Geographic Information System (GIS). We mapped the environmentally suitable areas for potato cultivation at a 3-km resolution based on the geo-climate model for each time period and analyzed differences between them. We found that "Most suitable” areas for potato cultivation were mainly distributed in the central area of Jilin Province, “Suitable” areas were located in the northwestern plains, and “Sub-suitable” areas were located in the eastern mountainous areas. In contrast, “Not suitable” areas occur mainly in the high-altitude areas in the east. The areas of “Most suitable” and “Suitable” areas for potato cultivation in Jilin Province were increasing, with increasing rates of 0.37 × 1,000 km2 decade−1 (R2 = 0.58, P < 0.01) and 0.20 × 1,000 km2 decade−1 (R2 = 0.28, P < 0.01), respectively, while the extent of “Sub-suitable” areas is decreasing, with a decreasing rate of 0.58 × 1,000 km2 decade−1 (R2 = 0.53, P < 0.05). The area of “Not suitable” areas had undergone little change. “Most suitable” and “Suitable” areas for potato cultivation showed a trend towards northward expansion. Overall, our results suggest that global climate change has had a positive impact on potato cultivation in Jilin Province over the past 58 years.

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“…Nevertheless, its success is strongly influenced by other factors including soil background, crop type, and light conditions [11]. Multi-temporal remote sensing monitoring across the growing seasons can uniquely offer insights into yield formation processes [12,13]. Although some positive results have been reported in potato yield prediction [14], the uncertainties of using only remote sensing data to estimate crop yield limit the application of the model [5,14,15].…”

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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Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Cited by 28 publications

References 60 publications

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

Impacts of climate change on suitability zonation for potato cultivation in Jilin Province, Northeast China

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

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