Assessment of Sugarcane Yield Potential across Large Numbers of Genotypes using Canopy Reflectance Measurements

Zhao, Duli; Gordon, Vanessa S.; Comstock, Jack C.; Glynn, Neil C.; Johnson, Richard M.

doi:10.2135/cropsci2015.12.0747

Cited by 15 publications

(9 citation statements)

References 40 publications

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“…Comparing NDVI, GNDVI, NDRE, and WDRVI, they all presented similar accuracy results for estimating the sugarcane yield. These results, except for NDRE, corroborate the results in Zhao et al [57] that reported no significant difference among the use of GNDVI, NDVI, and WDRVI for predicting sugarcane crop growth and yield. However, their approach based on linear regression models was different from the one in this study that tested both linear and non-linear models, as it was noted that the non-linear (RF regression) model performed better (higher R 2 , lower RMSE, and MAE), independent of the type of predictor variables.…”

Section: Discussionsupporting

confidence: 90%

“…Thus, they proceeded using the simple linear regression and found R 2 values ranging from 0.22 to 0.41. Comparing their result with this study, the MLR presented lower R 2 than those found in Zhao et al [57], even using a greater number of predictor variables (22 instead of 8). Despite the higher R 2 found in the MLR model, the RF regression is the model that outperformed the linear approach, highlighting its suitability to be used to support yield prediction models based on orbital images.…”

Section: Discussionsupporting

confidence: 55%

“…It was expected that linear approaches (MLR) that predict yield based on VIs would present worse accuracy performance than non-linear models (e.g., RF regression). Zhao et al [57] applied MLR with spectral bands and simple linear regression with NDVI to predict sugarcane yield. They found that MLR did not improve accuracy when compared to the simple linear regression.…”

Section: Discussionmentioning

confidence: 99%

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Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

et al. 2021

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Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

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Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

et al. 2021

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“…The authors of [41] used the NDVI, among other indices, to demonstrate the influence of water deficit in the low fruit production in apple trees. The work in [42] measures sugarcane yield potential across a large number of genotypes using canopy reflectance measurements, such as NDVI. Recently, Johansen et al [43] investigated the use of high-spatial resolution satellite image data and geographic object-based image analysis, based on NDVI, to map putative sugarcane grub damage.…”

Section: Calculating the Vegetation Indexmentioning

confidence: 99%

Spectral signature of brown rust and orange rust in sugarcane

Soca-Muñoz

Rodríguez-Machado

Aday-Díaz

et al. 2020

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Precision agriculture, making use of the spatial and temporal variability of cultivable land, allows farmers to refine fertilization, control field irrigation, estimate planting productivity, and detect pests and disease in crops. To that end, this paper identifies the spectral reflectance signature of brown rust (Puccinia melanocephala) and orange rust (Puccinia kuehnii), which contaminate sugar cane leaves (Saccharum spp.). By means of spectrometry, the mean values and standard deviations of the spectral reflectance signature are obtained for five levels of contamination of the leaves in each type of rust, observing the greatest differences between healthy and diseased leaves in the red (R) and near infrared (NIR) bands. With the results obtained, a multispectral camera was used to obtain images of the leaves and calculate the Normalized Difference Vegetation Index (NDVI). The results identified the presence of both plagues by differentiating healthy from contaminated leaves through the index value with an average difference of 11.9% for brown rust and 9.9% for orange rust.

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“…Rebetzke et al [12] considered early-stage plant vigour via larger specific leaf area as an indirect selection target in wheat, and more recently, Kipp et al [13] and Duan et al [14] reported a high-throughput scoring method for early vigour using visual images that may improve wheat selections at early growth stage. Further, a strong correlation of sugarcane stalk population and yield with normalised difference vegetation index (NDVI) and reflectance at specific wavelengths was reported, suggesting that canopy reflectance measurements at the early growth stage can be used as a screening tool to estimate yield potential [15]. In maize, combining data from 62 wavebands and vegetation indices measured across multiple times using aerial phenotyping lead to an increase in prediction accuracy compared with using single-time-point data [16].…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding

et al. 2019

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One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding.

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Assessment of Sugarcane Yield Potential across Large Numbers of Genotypes using Canopy Reflectance Measurements

Cited by 15 publications

References 40 publications

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Spectral signature of brown rust and orange rust in sugarcane

High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding

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