Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging

Abdelkrim Ouafi; Abdelmalik Taleb-Ahmed; Cosimo Distante; Fadi Abdeladhim Zidi; Fares Bougourzi

REVIEW 5 major objections 6 minor 196 references

Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This survey claims to be the first comprehensive review of deep learning methods on hyperspectral imaging for wheat crop analysis, organizing 193 studies into a structured taxonomy.

desk verdict A useful wheat-specific dataset collection buried under an irreproducible screening process and a corpus of generic HSI papers. read the letter →

arxiv 2505.00805 v1 pith:SWSYMCSG submitted 2025-05-01 cs.CV eess.IV

Fadi Abdeladhim Zidi , Abdelkrim Ouafi , Fares Bougourzi , Cosimo Distante , Abdelmalik Taleb-Ahmed This is my paper · ORCID

classification cs.CVeess.IV

keywords hyperspectralimagingdeeplearningwheatcropsdiseasedetectionyieldestimationnutrientliteraturesurveyprecisionagriculture

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review paper sets out to establish that deep learning applied to hyperspectral imaging (HSI) for wheat crops has grown into a distinct research area that deserves its own comprehensive survey. It claims to be the first review focused specifically on that intersection, covering work published from 2015 to 2024 and synthesizing 193 retained articles. The paper organizes the field into a taxonomy of learning paradigms—supervised, semi-supervised, and unsupervised—and maps each to model families such as CNNs, RNNs, Transformers, Mamba networks, DBNs, SAEs, GANs, and diffusion models. It then ties those methods to four application areas: crop classification, disease detection and monitoring, nutrient estimation, and yield estimation. A sympathetic reader would care because a reliable map of methods, datasets, and open problems could guide practitioners choosing models and expose where public data are lacking.

What carries the argument

The organizing device is a three-level taxonomy built from learning paradigm, model family, and application. Supervised learning covers CNNs, RNNs, DBNs, SAEs, Transformers, and Mamba; semi-supervised learning covers pseudo-labeling and generative adversarial networks; unsupervised learning covers DBNs, SAEs, and diffusion models. The taxonomy is cross-referenced to four wheat applications—classification, disease detection, nutrient estimation, yield estimation—and anchored by a dataset inventory that includes Indian Pines, GHISA, DRUM, and several Fusarium and yellow-rust collections. The taxonomy does the paper's main work: it turns a scattered literature into a structured map, and it is the basis for the claim that this intersection is a coherent field with identifiable trends and gaps.

What would settle it

Re-run the stated database searches (ScienceDirect, IEEE Xplore, SpringerLink, MDPI; 2015–2024; 'Hyperspectral Imaging' OR 'HSI' AND 'Deep Learning' AND 'Wheat Crops') and count how many of the 193 retained articles actually apply their method to wheat data. A decisive test: check the Section 5 method entries—for example the Mamba papers [80]–[85] and transformer papers [77]–[79]—and see whether wheat appears anywhere beyond a single class in the Indian Pines benchmark; if most never address wheat, the survey's wheat-specific claim would collapse.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that it provides the first comprehensive review of deep learning methods applied to hyperspectral imaging data for wheat crop analysis. To support that claim, it assembles and categorizes 193 papers from four literature databases, groups the methods by learning paradigm and architecture, and summarizes the benchmark datasets—six public and a set of private collections focused mainly on Fusarium head blight and yellow rust. The survey finds that classification dominates the literature at 68% of the retained work, with disease detection at 13.1%, nutrient estimation at 11.8%, and yield estimation at 7.2%, and it argues that the field's main bottlenecks are scarce labeled data and the computational cost of high-dimensional HSI processing.

Load-bearing premise

The load-bearing premise is that the screening process genuinely captured wheat-specific hyperspectral deep-learning studies and that the 193 retained papers were summarized faithfully; if many included papers only use a generic HSI benchmark containing a wheat class, or if the per-paper summaries misrepresent their methods, the survey's map is distorted.

Editorial extensions

If this is right

Practitioners get a single reference map of deep-learning models and datasets for hyperspectral wheat analysis, organized by learning paradigm and application.
The survey's statistics make the field's imbalance explicit: classification accounts for 68% of published work, while yield estimation is under-served at 7.2%.
Because most wheat-disease datasets are private, the paper's call for large, open, region-diverse datasets is a concrete agenda item for the community.
The review identifies Mamba and transformer architectures as the current state of the art and points to lightweight models and self-supervised learning as priority research directions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The survey treats general HSI benchmarks such as Indian Pines as wheat-relevant because they contain a wheat class; a stricter reading of 'wheat crop analysis' would exclude many of the cited architecture papers, so the claimed 193-article corpus likely overcounts wheat-specific studies.
If the field really is at the maturity the survey describes, a natural next step would be a shared benchmark with standardized splits and evaluation metrics for wheat-specific tasks, allowing reported accuracy numbers to be compared fairly across architectures.
The maintained companion repository could evolve into a living index; its usefulness is testable by whether it continues to be updated with post-2024 papers.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The best part of this survey is not the taxonomy—that's the standard supervised/semi-supervised/unsupervised split you've seen in other HSI reviews—but the wheat-specific tables. Tables 4 and 5 compile private Fusarium and yellow rust datasets with sensors, wavelengths, and key bands, and Tables 8–11 summarize wheat classification, disease detection, nutrient estimation, and yield estimation papers with concrete results. A practitioner looking for a starting point on wheat HSI would find those genuinely useful, and the GitHub tracking repo is a nice touch.

The problem is the survey's load-bearing claim of firstness. The screening is irreproducible. The text says 268 articles were discarded and 193 retained; Figure 2 says 294 excluded and 173 selected, and the figure labels the source as Google Scholar while the text names four databases. Those are not cosmetic errors; they make the methodology impossible to check.

Worse, the corpus isn't wheat-specific. The taxonomy and Tables 6–7 are full of generic HSI papers evaluated on Indian Pines, Pavia, Salinas, Botswana—papers like HybridSN, SSRN, and Mamba models that have no wheat experiment. The tables even report 'wheat crop class acc' as the per-class accuracy of the wheat class inside Indian Pines. That is a standard benchmark class, not wheat crop analysis. So the 'first wheat-specific review' claim is undercut by the paper's own content.

The self-citation doesn't help: reference [147] is the authors' own 2024 review on DL for HSI in agriculture, cited in passing without explaining how the present survey differs. That needs to be addressed directly.

There are also smaller errors—typos like '96.2.6%' and 'AO:IP=98.3%'—which, in a reference work, undermine trust.

So the verdict is reject in current form. But it's not worthless. The wheat-specific dataset collection and the application summaries are worth salvaging. If the authors redo the screening with a genuinely wheat-specific inclusion criterion, reconcile the counts, and clearly differentiate from their prior review, this could become a useful survey for the precision-agriculture community.

I'd send it to a serious peer review rather than desk-reject, because a referee can push for the needed rework and the underlying material has value. But I wouldn't cite it in its current state.

Referee Report

5 major / 6 minor

Summary. This manuscript is a survey of deep learning (DL) methods applied to hyperspectral imaging (HSI) for wheat crop analysis. It claims to be the first review focusing specifically on the intersection of HSI and DL for wheat crops, and it organizes the literature into supervised, semi-supervised, and unsupervised learning paradigms. The paper reviews hyperspectral sensors and platforms, public and private wheat datasets, DL architectures (CNNs, RNNs, Transformers, Mamba models, DBNs, SAEs, GANs, diffusion models), and applications such as wheat classification, disease detection, nutrient estimation, and yield estimation. It also discusses challenges and future directions and points to a GitHub repository for tracking state-of-the-art papers.

Significance. If the survey were accurate and comprehensive, it would be a valuable reference for the precision-agriculture and remote-sensing communities, bringing together datasets, a broad taxonomy of DL architectures, and application areas in a single place. The manuscript deserves credit for attempting a structured taxonomy, for compiling sensor and dataset tables, and for providing a public GitHub repository. However, the central claim of being the 'first review' on wheat-specific HSI+DL is not supported by the actual content: the methodology section gives irreproducible screening counts, and many retained papers are generic HSI classification studies evaluated on non-wheat benchmark scenes. These problems undermine the reliability of the survey as a reference and directly affect its core claim.

major comments (5)

[2 / Figure 2] The screening counts are inconsistent: Section 2 states that 268 articles were discarded and 193 retained, whereas Figure 2 reports 294 excluded and 173 selected, and Figure 2 labels the source as 'Google Scholar' rather than the four databases named in the text. Because the size and provenance of the corpus are central to the survey's comprehensiveness claim, this discrepancy makes the methodology irreproducible and needs to be resolved with a single consistent PRISMA-style accounting.
[5.1-5.3 / Tables 6-7] Many entries in the 51-item taxonomy correspond to generic HSI classification papers evaluated on benchmark scenes such as Indian Pines, Pavia, Salinas, Botswana, and KSC, with no wheat-crop experiment; examples include references [66, 67, 68, 70, 72, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 101, 102, 103, 104, 105, 107, 108, 111, 112, 113]. The 'wheat crop class acc' column in Tables 6-7 reports per-class accuracy for the wheat class inside Indian Pines, which is a land-cover benchmark, not a wheat-crop analysis. This directly contradicts the inclusion criteria stated in Section 2 and undermines the claim that the survey is wheat-specific.
[4.1 / Table 3] Table 3 is presented as a summary of public wheat-crop analysis datasets, but it includes Indian Pines and GHISA, which are general agricultural datasets where wheat is only one class among many. The text says the table includes 'six specifically related to wheat crop analysis,' yet only four entries (Soilborne Wheat Mosaic Virus, Wheat HyperSpectral, Early Detection of Crown Rot, DRUM) are wheat-focused. This mismatch further weakens the wheat-specific scope and makes the dataset inventory misleading.
[5.4 / Table 6] Table 6 contains numerical errors that undermine its reliability as a reference, such as 'OA:IP=96.2.6%' for reference [86] and several rows reporting wheat per-class accuracy of 100% (e.g., references [68] and [79]). Combined with the inclusion of non-wheat benchmark papers, this suggests the summary tables were assembled without careful verification of the original studies.
[1 / reference [147]] The introduction claims that this is the first review focused specifically on HSI and DL for wheat crop analysis, but reference [147] is a 2024 review by the same research group on deep learning for hyperspectral image analysis in agriculture. The manuscript does not explain how the present survey differs from that prior review, so the novelty and firstness claims are not properly supported.

minor comments (6)

[Abstract] The phrase 'extract and analysis complex structures' should be corrected to 'extract and analyze complex structures.'
[Figure 11] The caption of Figure 11 reads 'Number of Published Articles by Year on DL with Hyperspectral Data in Wheat Crops,' but the figure itself is a pie chart showing the percentage distribution of application categories; these do not match.
[Throughout] There are numerous typographical and formatting errors, including 'have have' in Section 5.2.2, 'Univerity' in the affiliations, inconsistent 'UA V' versus 'UAV' spacing, and 'lLSTMs' in Section 7.
[Tables 1-2 / text] The sensor name is written inconsistently as 'A VIRIS' and 'AVIRIS'; the standard spelling is AVIRIS.
[Table 3] The abbreviation 'VNI' is defined as 'Very Near Infrared'; the standard abbreviation is VNIR.
[Abstract / Repository] The GitHub repository URL appears only in the abstract; it would be helpful to include it in the conclusion or a dedicated data-availability statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this survey contains no derived equations or fitted quantities, and its firstness claim is an external scoping assertion rather than an output of the screened corpus.

full rationale

This is a literature survey, not a derivation. There are no fitted parameters, no predictive equations, and no formal model whose output is built from its own input. The central novelty claim, 'To the best of our knowledge, this is the first review to focus specifically on the intersection of HSI data and DL techniques for wheat crop analysis' (Section 1), is a scoping statement about the literature, not a mathematical consequence of the survey's own contents. The screening methodology in Section 2 is a selection procedure, and the retained papers are summarized rather than used as inputs to a calculation whose outcome is then reported as a prediction. The only overlapping-author citation is reference [147], a prior review by Guerri, Distante, Spagnolo, Bougourzi, and Taleb-Ahmed. It appears once, in Section 5.3.1, to support the uncontroversial statement that stacked autoencoders 'excel in reducing dimensionality while preserving critical spectral-spatial details.' This citation is not load-bearing: the survey's organization, taxonomy, and application summaries do not depend on that reference, and the firstness claim does not rest on it. A reviewer could remove [147] without changing any conclusion of the survey. The skeptical concerns about wheat-specificity (e.g., inclusion of Indian Pines benchmark results and reporting 'wheat crop class acc' as per-class accuracy within Indian Pines) are substantive correctness or scoping criticisms, but they are not circularity. They concern whether the survey's corpus matches its stated scope, not whether a claim is equivalent to its own premises by construction. Similarly, discrepancies between the text (268 discarded, 193 retained) and Figure 2 (294 excluded, 173 selected) are internal-consistency and reproducibility issues, not circular reasoning. Accordingly, the paper is best described as containing no significant circularity: its claims are literature-based, externally checkable, and not reduced to self-citation or definitional equivalence.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This is a survey, so there are no fitted parameters or invented entities. The key assumptions are that the literature selection is accurate and that the first-survey claim is true. Both are called into question by internal inconsistencies and an unreconciled prior review from the same group.

assumptions (2)

domain assumption The 193 retained articles are relevant to analyzing wheat crops from HSI data and are accurately summarized.
The survey's value depends on accurate screening and representation of the literature. Section 2 describes the screening, but the results include off-topic general HSI classification papers and the flowchart contains inconsistent counts.
ad hoc to paper No prior survey focuses specifically on HSI plus deep learning for wheat crops.
The paper claims firstness in Section 1, yet cites reference 147, a 2024 review by overlapping authors on deep learning for HSI in agriculture, which plausibly covers wheat. The relationship is not acknowledged.

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Cite this review

Pith. "Pith review of Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging." pith.science (2026). https://pith.science/paper/SWSYMCSG

@misc{pith2026250500805,
  author       = {Pith},
  title        = {Pith review of: Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SWSYMCSG}},
  note         = {Machine review of arXiv:2505.00805}
}

read the original abstract

As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following https://github.com/fadi-07/Awesome-Wheat-HSI-DeepLearning.

Figures

Figures reproduced from arXiv: 2505.00805 by the authors.

**Figure 2.** Overview of the Methodological Strategy for Articles Search and Selection in Conducting this Survey. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** AVIRISng (Airborne Visible Infrared Imaging Spectrometer next generation) HSI cube of Mount Vesuvius, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: Number of Published Articles on DL Models by Year on Hyperspectral Data in Wheat Crops. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

**Figure 5.** Figure 5: Taxonomy for Deep Learning Methods for Wheat Crops from HSI Data: [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: 3D CNN Architecture Optimized for Spectral-Spatial Feature Extraction [115]. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]

**Figure 7.** Figure 7: Optimized Stacked Autoencoder Architecture for HSI [126]. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

**Figure 8.** Figure 8: The overall structure of the proposed model using the transformer architecture [76]. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

**Figure 9.** Figure 9: The architectural framework delineating the initial Generative Adversarial Network (GAN) comprises two [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

**Figure 10.** Figure 10: Graphical representation of the DBN for HSI classification [89]. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]

**Figure 11.** Figure 11: Number of Published Articles by Year on DL with Hyperspectral Data in Wheat Crops. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]

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Reference graph

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X. Wan, C. Zhao, Y . Wang, and W. Liu, “Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features,”Infrared Physics & Technology, vol. 86, pp. 77–89, 2017

2017
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Unsupervised feature-learning for hyperspectral data with autoencoders,

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Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm,

P. S. Singh and S. Karthikeyan, “Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm,”Neural Computing and Applications, vol. 34, no. 24, pp. 21 539–21 550, 2022

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2017
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Spatial-spectral transformer for hyperspectral image classification,

X. He, Y . Chen, and Z. Lin, “Spatial-spectral transformer for hyperspectral image classification,”Remote Sensing, vol. 13, no. 3, p. 498, 2021

2021
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Efficiently modeling long sequences with structured state spaces,

A. Gu, K. Goel, and C. Ré, “Efficiently modeling long sequences with structured state spaces,”arXiv preprint arXiv:2111.00396, 2021

2021 arXiv
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Mamba: Linear-time sequence modeling with selective state spaces,

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2023 arXiv
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Semi-supervised superpixel-based multi-feature graph learning for hyperspectral image data,

M. S. Kotzagiannidis and C.-B. Schönlieb, “Semi-supervised superpixel-based multi-feature graph learning for hyperspectral image data,”IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2021

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Prototype-based pseudo-label refinement for semi-supervised hyperspectral image classification,

R. Chen, H. Yao, W. Chen, H. Sun, W. Xie, L. Dong, and X. Lu, “Prototype-based pseudo-label refinement for semi-supervised hyperspectral image classification,”IEEE Geoscience and Remote Sensing Letters, 2024

2024
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Semisupervised variational generative adversarial networks for hyperspectral image classification,

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Data-efficient domain adaptation for semantic segmentation of aerial imagery using generative adversarial networks,

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2020
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A general generative adversarial capsule network for hyperspectral image spectral-spatial classification,

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2020
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A hyperspectral image classification method based on multi-discriminator generative adversarial networks,

H. Gao, D. Yao, M. Wang, C. Li, H. Liu, Z. Hua, and J. Wang, “A hyperspectral image classification method based on multi-discriminator generative adversarial networks,”Sensors, vol. 19, no. 15, p. 3269, 2019

2019
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Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology,

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Dimensionality reduction of hyperspectral imagery based on spatial–spectral manifold learning,

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Classification of hyperspectral image based on deep belief networks,

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Change vector analysis: A technique for the multispectral monitoring of land cover and condition,

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Unsupervised hyperspectral image change detection via deep learning self-generated credible labels,

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Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images,

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2016
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Current state of hyperspectral remote sensing for early plant disease detection: A review,

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2022
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Unsupervised spectral–spatial feature learning via deep residual conv– deconv network for hyperspectral image classification,

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Binary change guided hyperspectral multiclass change detection,

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2023
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Unsupervised deep slow feature analysis for change detection in multi- temporal remote sensing images,

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Hypernet: Self-supervised hyperspectral spatial–spectral feature understanding network for hyperspectral change detection,

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Convolutional neural networks for agricultural land use classification from sentinel-2 image time series,

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2022
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A novel statistical metric learning for hyperspectral image classification,

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1905 arXiv
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Semisupervised hyperspectral image classification based on generative adversarial networks,

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Wheat seed classification using neural network pattern recognizer,

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2023
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Identification of wheat tiller based on alexnet-feature fusion,

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2022
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Wheat kernels,

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2023
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Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks,

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2022
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Image-based wheat grain classification using convolutional neural network,

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2021
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Deep convolutional neural networks for hyperspectral image classification,

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Hyperspectral image classification with convolutional neural networks,

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2020
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Near-infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains,

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2018
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Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics,

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2019
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Wheat kernels classification using visible-near infrared camera based on deep learning,

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Identification of slightly sprouted wheat kernels using hyperspectral imaging technology and different deep convolutional neural networks,

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2023
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Identification of wheat kernel varieties based on hyperspectral imaging technology and grouped convolutional neural network with feature intervals,

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2023
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Hyperspectral sensing of plant diseases: Principle and methods,

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2022
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Hyperspectral remote sensing for early detection of wheat leaf rust caused by puccinia triticina,

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2023
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Wheat fusarium head blight automatic non-destructive detection based on multi-scale imaging: A technical perspective,

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2024
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Classifying wheat hyperspectral pixels of healthy heads and fusarium head blight disease using a deep neural network in the wild field,

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Mce-st: Classifying crop stress using hyperspectral data with a multiscale conformer encoder and spectral-based tokens,

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Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat,

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2021
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Detection of fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning,

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Detection of fusarium head blight in wheat using hyperspectral data and deep learning,

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Hyperspectral monitoring of powdery mildew disease severity in wheat based on machine learning,

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2022
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Wheat leaf disease identification based on deep learning algorithms,

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2023
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Development of methods for remote monitoring of leaf diseases in wheat agrocenoses,

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2023
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The potential of uav and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in ethiopia,

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Recognition of wheat rusts in a field environment based on improved densenet,

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2024
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Applications of a hyperspectral imaging system used to estimate wheat grain protein: A review,

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A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture,

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2023
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Predicting micronutrients of wheat using hyperspectral imaging,

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Assessment of wheat chlorophyll content by the multiple linear regression of leaf image features,

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Rapid and nondestructive evaluation of wheat chlorophyll under drought stress using hyperspectral imaging,

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Comparison of attention mechanism- based deep learning and transfer strategies for wheat yield estimation using multisource temporal drone imagery,

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2024

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

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Deep belief network for spectral–spatial classification of hyperspectral remote sensor data,

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Hypersformer: A transformer- based end-to-end hyperspectral image classification method for crop classification,

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2023

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Crops that feed the world 10. past successes and future challenges to the role played by wheat in global food security,

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The role of wheat in global food security,

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2018

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Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the hyspiri mission,

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2013

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Advantage of hyperspectral eo-1 hyperion over multispectral ikonos, geoeye- 1, worldview-2, landsat etm+, and modis vegetation indices in crop biomass estimation,

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2015