Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield

Aguate, Fernando; Trachsel, Samuel; Pérez, Lorena González; Burgueño, Juan; Crossa, José; Balzarini, Mónica; Gouache, David; Bogard, Matthieu; Campos, Gustavo de los

doi:10.2135/cropsci2017.01.0007

Cited by 80 publications

(89 citation statements)

References 36 publications

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“…Moreover, computer vision with its advantages of high precision and intelligence attracted it as an alternative to human inspection. This technology was a dramatic boost for pest detection (Boissard et al, 2008;Shahin and Symons, 2011;Ding and Taylor, 2016;Senthilkumar et al, 2017), growth monitoring (Clevers and Leeuwen, 1996;Chaerle and Straeten, 2000;Wang et al, 2013;Silva et al, 2014), yield prediction (Salazar et al, 2007;Dunn and Martin, 2010;Aggelopoulou et al, 2011;Aguate et al, 2017) and species recognition (Neuman et al, 1987;Lópezgranados et al, 2006;Tellaeche et al, 2011;Pantazi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images

Tan

Zhang

et al. 2020

Front. Plant Sci.

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Wheat spike number, which could be rapidly and accurately estimated by the image processing technology, serves as the basis for crop growth monitoring and yield prediction. In this research, simple linear iterative clustering (SLIC) was performed for superpixel segmentation of the digital images of field-grown wheat. Firstly, certain characteristic color parameters were extracted and analyzed from the digital images, and the classifiers with the highest accuracy were chosen for subsequent image classification. Next, the main body of wheat spike was extracted through a series of morphological transformation and estimate was performed for each region. Backbone of the head was extracted, and the number of inflection points of backbone was detected. Then the wheat spike number was determined by combining the estimate of inflection points of backbone and the estimate for each region. Finally, the wheat spike number estimate was verified under four nitrogen fertilizer levels. The results were as follows: (1) Super green value (Eg) and normalized red green index (Dgr) were used as classification features to recognize wheat spikes, soil and leaves; (2) compared with pixel-based image processing, wheat spike recognition effect was much better after superpixel segmentation, as the main body of wheat spike extracted was more clear and morphology more intact; and (3) wheat plants had better growth under high nitrogen fertilizer level, and the accuracy of wheat spike number estimation was also the highest, which was 94.01%. The growth status was the worst under no nitrogen fertilizer application, and the accuracy of wheat spikes number estimation was also the lowest, which was only 80.8%. After excluding the no nitrogen condition, the accuracy of wheat spikes number estimation among mixed samples with more uniform growth status was up to 93.8%, which was an increase by 10.1% than before the exclusion. Wheat spikes number estimate based on superpixel segmentation and color features was a rapid and accurate method that was applicable to the field environment. However, this method was not recommended for use when the growth status of wheat was poor or of high heterogeneity. The findings provided reference for field-grown wheat yield estimate.

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Section: Introductionmentioning

confidence: 99%

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images

Tan

Zhang

et al. 2020

Front. Plant Sci.

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“…Jarquin et al [10] utilized the reaction norm model for genomic prediction where the genetic and environmental values were replaced by the regression on the markers, and in the interaction between the markers and the environmental covariates, respectively. Dealing with high-throughput phenotypic information, several authors [11][12][13] have shown improvements in predictive ability with the inclusion of these sources of information in the models for wheat and maize. Montesinos-Lopez et al [14] showed that accounting for the band (hyper-spectral image data)-by-environment interaction also improved yield predictability in wheat when compared with those models that did not include this component in the models.…”

Section: Introductionmentioning

confidence: 99%

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

et al. 2018

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Abstract:Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14-71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together with the inclusion of several types of interactions between canopy coverage data, environments, and genomic data. Three different scenarios were considered that breeders might face testing lines in fields: (i) incomplete field trials (CV2); (ii) newly developed lines (CV1); and (iii) predicting lines in unobserved environments (CV0). Two different traits were evaluated in this study: yield and days to maturity (DTM). Results showed improvements in the predictive ability for yield with respect to those models that solely included genomic data. These relative improvements ranged 27-123%, 27-148%, and 65-165% for CV2, CV1, and CV0, respectively. No major changes were observed for DTM. Similar improvements were observed for both traits when the reduced canopy information for days 14-33 was used to build the training-testing relationships, showing a clear advantage of using phenomics in very early stages of the growing season.

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“…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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Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield

Cited by 80 publications

References 36 publications

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

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

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