Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis

Ali Moghimi; Alireza Pourreza; Hamid Jafarbiglu; Mohammadreza Narimani; Mohsen Mesgaran; Parastoo Farajpoor

REVIEW 4 major objections 6 minor 1 cited by

Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis

T0 review · 4 major / 6 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read This paper proposes an end-to-end pipeline that detects branched broomrape infestation in tomato farms by analyzing Sentinel-2 satellite time series with a Long Short-Term Memory network, reporting 87% test accuracy on individual vegetation

desk verdict Useful pipeline and promising direction, but the pixel-level split with only 10 fields inflates the headline accuracy, so the paper needs field-level validation before its claims hold up. read the letter →

arxiv 2509.10804 v1 pith:RH7G367H submitted 2025-09-13 eess.IV cs.AIcs.CVcs.LG

Mohammadreza Narimani , Alireza Pourreza , Ali Moghimi , Parastoo Farajpoor , Hamid Jafarbiglu , Mohsen Mesgaran This is my paper · ORCID

classification eess.IVcs.AIcs.CVcs.LG

keywords branchedbroomrapetomatoSentinel-2LSTMtimeseriesvegetationindicesplanttraitsremotesensing

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

Branched broomrape is a root parasite that can destroy up to 80% of a tomato crop, but it spends most of its life underground and its seeds persist for decades, so spotting it early is hard. This paper claims that an LSTM network trained on time series of Sentinel-2 satellite data can classify individual vegetation pixels as infested or healthy with 87% test accuracy, by aligning fields on thermal time and using spectral indices and neural-network-derived plant traits. The features that matter most—NDMI, Canopy Chlorophyll Content, FAPAR, and Chlorophyll Red-Edge—are exactly the signals one would expect from a parasite that drains water and chlorophyll. The authors position this as a scalable early-warning system that could complement field scouting and reduce blanket herbicide use.

What carries the argument

The mechanism is a per-pixel time-series classifier: 37 input features (12 Sentinel-2 bands, 20 vegetation indices, and five plant traits) are arranged over 48 growing-degree-day time points and fed into a two-layer LSTM (64 and 32 units, with dropout and a binary output), about 39,600 parameters. Growing degree days provide a thermal clock that synchronizes fields with different transplanting and harvest dates; the five plant traits, derived from a neural network trained on Sentinel-2 geometry, also drive a PCA/K-means segmentation that restricts analysis to vegetation pixels.

What would settle it

Retrain the model with leave-one-field-out cross-validation (train on nine fields, test on the tenth); if accuracy drops substantially on held-out fields, the per-pixel split inflated the result. A second check is to apply the trained model to a completely unseen tomato field in a different region and compare predictions with on-the-ground infestation surveys.

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Extended reading notes

Core claim

The central claim is that phenology-aligned satellite time series, not any single image, carry the detectable signature of broomrape infestation. By resampling each tomato field onto 48 growing-degree-day steps, training a two-layer LSTM on per-pixel feature stacks, and masking out non-vegetation, the model reaches 88% training and 87% test accuracy, with precision 0.86, recall 0.92, and F1 0.89. Permutation importance singles out NDMI, Canopy Chlorophyll Content, FAPAR, and Chlorophyll Red-Edge as the decisive features, and density plots confirm that healthy fields show higher values for all four. The paper interprets this as evidence that the model captures the parasite's physiological eff

Load-bearing premise

The load-bearing assumption is that the per-pixel train/test split gives independent test points, even though all pixels come from just ten fields and neighboring pixels share soil, management, and imaging artifacts; if pixels within a field are not independent, the 87% test accuracy does not measure how well the model detects infestation in a field it has never seen.

Editorial extensions

If this is right

If the accuracy generalizes to unseen fields, growers could prioritize scouting in satellite-flagged zones rather than applying herbicides uniformly.
The same LSTM pipeline, with retraining, could be transferred to other parasitic weeds (e.g., sunflower broomrape) or other crops with similar stress signatures.
The feature-importance results suggest a leaner monitoring system could focus on a few indices (NDMI, CCC, FAPAR, red-edge chlorophyll), possibly with cheaper sensors.
Pixel-level labels would allow mapping infestation intensity within a field, enabling site-specific treatment and a quantitative basis for management decisions.

Reading between the lines

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

The 87% test accuracy is probably optimistic because the split samples pixels, not fields; all pixels in the test set come from the same ten fields as the training set, so spatially correlated conditions (soil, irrigation, microclimate) may be memorized. A leave-one-field-out evaluation would give a fairer estimate of generalization.
The ground truth is farmer-reported infestation at the field level, so the pixel labels inherit whatever bias exists in when and how infestations were identified; the model may be learning late-stage signals rather than the early detection the paper emphasizes.
The feature-importance ranking points toward a much simpler hydrological and chlorophyll-stress detector; an editor's guess is that NDMI and CCC alone could reproduce most of the accuracy, which is worth testing before deploying the full LSTM.
The pipeline's scalability claim is untested beyond ten California processing-tomato fields; performance in other climates, varieties, and irrigation regimes remains an open question.

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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

I read Narimani et al. on satellite-based broomrape detection. The main thing you should know: the evaluation design undercuts the headline 87% accuracy, so the paper's central claim is not yet supported. But the pipeline itself is sensible and the application is genuinely new—Sentinel-2 time series plus LSTM for branched broomrape in tomato has not been done before, and the feature-importance results line up with the physiology.

What the paper does well: it assembles a full pipeline—Sentinel-2 bands, 20 VIs, five neural-network traits from S2ToolBox, GDD-based phenology alignment, vegetation masking via PCA and K-means, and an LSTM classifier. The use of GDD to align fields at equivalent growth stages is a good idea. The permutation importance analysis, pointing to NDMI and canopy chlorophyll, is consistent with broomrape's effect on water and chlorophyll. That part is credible.

The soft spot is the split. The paper says 18,874 pixels from 10 fields (5 infested, 5 healthy) and splits 65/15/30 randomly across pixels. That is a classic data leakage setup: pixels from the same field are autocorrelated, so the test set contains near-duplicates of the training set. Effective sample size is ten fields, not thousands of pixels. The reported 87% test accuracy likely reflects field memorization, not generalizable detection. On top of that, the split percentages sum to 110%, which suggests a typo or confusion about the protocol, and the text says "five-fold cross-validation" right before describing a single split—these need to be reconciled. No error bars or field-level cross-validation are provided, so you cannot tell how the model would do on a completely new field.

The labels themselves are also field-level: all pixels in a farmer-reported infested field are positive. The model may simply learn field-level differences in soil, management, or image acquisition. That is fine if you want to flag fields, but not pixel-level infestation.

I would not reject the idea out of hand. The problem is real, the satellite-based approach is scalable, and the pipeline is reusable. What is missing is an honest evaluation: field-level leave-one-out cross-validation, or at least a clear statement that this is a field-discrimination model with a small sample. With that, the paper could be a useful proof-of-concept.

Who is this for? Researchers working on parasitic weed detection or time-series remote sensing. It is a conference paper, so expectations should match. It deserves a serious referee, but the referee should push for major revision on the evaluation before any accuracy claims are published.

Recommendation: send it to review, but expect a request for field-level validation and clearer reporting of the data split.

Referee Report

4 major / 6 minor

Summary. The paper presents an end-to-end pipeline for detecting branched broomrape infestation in California tomato fields using Sentinel-2 satellite imagery. For five farmer-reported infested and five non-infested fields, the authors compute 12 spectral bands, 20 vegetation indices, and five neural-network-derived plant traits over 48 growing-degree-day (GDD) time steps. Vegetation pixels are segmented by PCA plus K-means clustering, and an LSTM classifier is trained on 18,874 pixels to label each pixel as infested or non-infested. The model is reported to achieve 88% training accuracy, 87% test accuracy, precision 0.86, recall 0.92, and F1 0.89. Permutation feature importance highlights NDMI, CCC, FAPAR, and CHL-RED-EDGE, consistent with known water and chlorophyll effects of broomrape.

Significance. If the reported accuracy were a valid estimate of performance on unseen fields, the pipeline would be a valuable, scalable tool for parasitic-plant detection in tomato. The paper integrates several sensible components: public Sentinel-2 data, GDD-based phenological alignment, a physically motivated feature set, and a recurrent classifier. However, the central quantitative claim is undermined by an evaluation design that treats pixels within only ten fields as independent samples. With no field-level cross-validation or external validation, the 87% test accuracy cannot be interpreted as evidence of generalizable infestation detection. The biological plausibility of the feature importance results is a useful qualitative signal, but it does not compensate for the statistical flaw in the evaluation.

major comments (4)

[§2.2 and §3] The train/test split is applied at the pixel level, not the field level. All 18,874 pixels come from only 10 fields (5 infested, 5 healthy), and the 65/15/30 split across pixels means that pixels from the same field appear in both training and test sets. Because pixels within a field are spatially autocorrelated and share field-specific soil, management, and imaging conditions, the held-out test pixels are not independent of training pixels. The effective sample size is 10 fields, not 18,874 pixels. The reported 87% accuracy likely reflects memorization of field-specific spectral signatures rather than a generalizable infestation signal. A field-level leave-one-field-out (or at least grouped) evaluation is required to support the paper's central claim.
[§2.1, §2.2] Ground-truth labels are farmer-reported field-level infestation status, and all vegetation pixels in an infested field are assigned the positive label. This makes the task effectively field discrimination projected onto pixels, not true pixel-level infestation detection. Infestation is unlikely to be spatially uniform across a field, and label noise at the pixel level is ignored. Moreover, the model may exploit field-level covariates such as soil type, irrigation, or image acquisition date to distinguish fields, confounding the learned signal. The paper needs either pixel-level ground truth or a clear statement that the model detects field-level infestation status, with evaluation at the field level.
[§2.2] The data splitting description is internally inconsistent. The text states a split of 65% training, 15% validation, and 30% testing, which sums to 110%, and also calls the procedure five-fold cross-validation. These two descriptions cannot both be correct. In addition, no error bars, confidence intervals, or per-fold/per-field performance breakdowns are reported, so it is impossible to assess the variance of the accuracy estimate. The reported single-point metrics are not sufficient evidence of robustness.
[Abstract and §3] The paper emphasizes 'early detection' as a key motivation and objective, but the experiments use the full 48-GDD time series, including peak vegetation and harvest stages. No experiment evaluates performance when the model is restricted to early-season GDD windows or assesses how detection accuracy evolves over the season. As presented, the system is a full-season classifier, not an early-warning detector. The abstract's early-detection framing is therefore not supported by the reported results.

minor comments (6)

[§2.2] The GDD base temperature is not specified. The text says 'a base temperature critical for tomato development' but never gives the value; this is a tunable parameter that affects all subsequent phenological alignment.
[§3 and §4] The conclusion contains multiple typographical errors: 'time-seriies', 'prommissing', and 'brooomrape'. The manuscript should be carefully proofread.
[§2.2] No class-balance information is provided. The reader cannot tell whether the 18,874 pixels are balanced between infested and non-infested fields, which affects precision, recall, and F1 interpretation.
[Appendix Table A2] Reference [8] for the S2ToolBox neural network traits is incomplete and informal; a full citation with version and access details is needed.
[Figure 2] The caption says '37 features' and the text lists 12 bands + 20 indices + 5 traits = 37, but the text also mentions sun/sensor angles as inputs in §2.1. Clarify whether angles are part of the 37 features or used only in the trait model.
[§2.2] The term 'five-fold cross-validation' is used alongside a fixed train/validation/test split. Please clarify the actual resampling procedure, including whether folds are defined by fields or pixels.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the empirical pipeline is self-contained, with only a minor non-load-bearing self-citation.

full rationale

The paper is an empirical classification study rather than a derivation. The ground-truth labels come from farmer-reported field infestations, independent of the spectral and trait features. The plant traits are obtained from an external pretrained model (S2ToolBox, reference [8]), not fitted to the infestation outcome. The LSTM is trained on pixel-level time series and evaluated on a held-out pixel set; no fitted constant or derived quantity is renamed as a prediction. The permutation feature importance and kernel-density comparisons are post-hoc analyses of the trained model and data, not circular inputs. The only self-citation is reference [6], the authors' prior drone-based study, used as background motivation; it is not load-bearing for the central claim. Concerns about the pixel-level split across only ten fields and the internal inconsistency of the 65/15/30 split are validity/correctness issues, not circularity: they do not make the reported accuracy equivalent to an input by construction. Therefore, no circular step is identified; the minor self-citation justifies a score of 1 rather than 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

No new physical entities are introduced. The model depends on several hand-chosen thresholds and external pretrained components, but no new constants or particles are postulated. The most consequential assumptions are the reliability of farmer-reported labels and the trait model, plus the pixel-independence assumption discussed in the weakest_assumption field.

free parameters (5)

GDD base temperature = Not stated in text
The base temperature for tomato GDD is chosen from literature but not specified, and no sensitivity analysis is provided.
Number of GDD time points (48) = 48
The paper compiles features across 48 GDD-based time steps, but the selection or interpolation of these time points is not justified.
Cloud cover threshold = 10%
Images with less than 10% cloud cover are retained; this threshold is chosen ad hoc and not tested.
K-means number of clusters = 2 (vegetation/background)
PCA plus K-means is used to segment vegetation, with k assumed to be 2 without validation.
LSTM hyperparameters = 64 and 32 units, dropout, 100 epochs
Architecture choices are hand-selected; no hyperparameter search or ablation is reported.

assumptions (3)

domain assumption Farmer-reported infestations provide accurate ground truth
Fields are labeled infested or non-infested based on farmer reports (Section 2.1), but no quantitative verification of infestation severity or extent is performed.
domain assumption Pretrained plant trait model (S2ToolBox) produces reliable traits for tomato canopies
The paper uses an external neural network to estimate LAI, Cab, CCC, FAPAR, and FCOVER from Sentinel-2 bands and geometry, but does not validate these estimates against ground measurements in this study.
domain assumption CCC maximum marks peak vegetation stage
The paper uses the maximum CCC value to indicate peak vegetation and align fields, but this is asserted without supporting data on tomato phenology.

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Cite this review

Pith. "Pith review of Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis." pith.science (2026). https://pith.science/paper/RH7G367H

@misc{pith2026250910804,
  author       = {Pith},
  title        = {Pith review of: Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RH7G367H}},
  note         = {Machine review of arXiv:2509.10804}
}

read the original abstract

Branched broomrape (Phelipanche ramosa (L.) Pomel) is a chlorophyll-deficient parasitic plant that threatens tomato production by extracting nutrients from the host, with reported yield losses up to 80 percent. Its mostly subterranean life cycle and prolific seed production (more than 200,000 seeds per plant, viable for up to 20 years) make early detection essential. We present an end-to-end pipeline that uses Sentinel-2 imagery and time-series analysis to identify broomrape-infested tomato fields in California. Regions of interest were defined from farmer-reported infestations, and images with less than 10 percent cloud cover were retained. We processed 12 spectral bands and sun-sensor geometry, computed 20 vegetation indices (e.g., NDVI, NDMI), and derived five plant traits (Leaf Area Index, Leaf Chlorophyll Content, Canopy Chlorophyll Content, Fraction of Absorbed Photosynthetically Active Radiation, and Fractional Vegetation Cover) using a neural network calibrated with ground-truth and synthetic data. Trends in Canopy Chlorophyll Content delineated transplanting-to-harvest periods, and phenology was aligned using growing degree days. Vegetation pixels were segmented and used to train a Long Short-Term Memory (LSTM) network on 18,874 pixels across 48 growing-degree-day time points. The model achieved 88 percent training accuracy and 87 percent test accuracy, with precision 0.86, recall 0.92, and F1 0.89. Permutation feature importance ranked NDMI, Canopy Chlorophyll Content, FAPAR, and a chlorophyll red-edge index as most informative, consistent with the physiological effects of infestation. Results show the promise of satellite-driven time-series modeling for scalable detection of parasitic stress in tomato farms.

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

17 extracted references · 5 canonical work pages · cited by 1 Pith paper

[1]

The parasite’s life cycle is predominantly subterranean, making early detection challenging

INTRODUCTION Branched broomrape ( Phelipanche ramosa ) is a parasitic plant that poses a significant threat to tomato cultivation by attaching to the host’s roots and siphoning essential nutrients, potentially leading to yield reductions of up to 80% [1]. The parasite’s life cycle is predominantly subterranean, making early detection challenging. Furtherm...

2025
[2]

METHODOLOGY 2.1. INTEGRATED SATELLITE DATA PROCESSING AND FEATURE EXTRACTION FOR PHENOLOGICAL ANALYSIS Five broomrape infested (identified and reported by farmers) tomato fields were compared to five non -infested fields in the same region. For each field, Sentinel imagery across the tomato growing season were monitored, capturing key phenological stages ...
[3]

RESULTS AND DISCUSSION The LSTM model was trained for 100 epochs using five-fold cross-validation, with the dataset split into 65% for training, 15% for validation, and 30% for testing. As shown in Figures 3a and 3b, both training and validation accuracy steadily increased, reaching a final training accuracy of 88%, while the corresponding loss curves dec...
[4]

CONCLUSION This study demonstrates a scalable approach for detection of branched broomrape infestation in tomato farms using Sentinel-2 satellite imagery and time -series analysis. A LSTM model, trained on time -seriies spectral feature, achieved promissing performance (88% training accuracy and 87% test accuracy, with precision, recall, and F1 scores of ...
[5]

O. A. Osipitan, B. D. Hanson, and Y. Goldwasser, “UC Agriculture & Natural Resources California Agriculture Title The potential threat of branched broomrape for California processing tomato: A review Permalink Publication Date Copyright Information,” 2021, doi: 10.3733/ca2021a0012

work page doi:10.3733/ca2021a0012 2021
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Using the possibilities of some trap, catch and Brassicaceaen crops for controlling crenate broomrape a problem in lentil fields,

E. Aksoy, Z. F. Arslan, Ö. Tetik, and S. Eymirli, “Using the possibilities of some trap, catch and Brassicaceaen crops for controlling crenate broomrape a problem in lentil fields,” Online, 2016. [Online]. Available: www.ijpp.info

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Leveraging time -based spectral data from UAV imagery for enhanced detection of broomrape in sunflower,

G. Atsmon, A. Brook, T. A. Cohen, F. Kizel, H. Eizenberg, and R. N. Lati, “Leveraging time -based spectral data from UAV imagery for enhanced detection of broomrape in sunflower,” Smart Agricultural Technology, vol. 10, Mar. 2025, doi: 10.1016/j.atech.2025.100809

arXiv 2025

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Hyperspectral imaging facilitates early detection of Orobanche cumana below-ground parasitism on sunflower under field conditions,

G. Atsmon, O. Nehurai, F. Kizel, H. Eizenberg, and R. Nisim Lati, “Hyperspectral imaging facilitates early detection of Orobanche cumana below-ground parasitism on sunflower under field conditions,” Comput Electron Agric, vol. 196, May 2022, doi: 10.1016/j.compag.2022.106881

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Drone -based multispectral imaging and deep learning for timely detection of branched broomrape in tomato farms,

M. Narimani, A. Pourreza, A. moghimi, M. B Mesgaran, P. Farajpoor, and H. jafarbiglu, “Drone -based multispectral imaging and deep learning for timely detection of branched broomrape in tomato farms,” SPIE -Intl Soc Optical Eng, Jun. 2024, p. 5. doi: 10.1117/12.3021219

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J. Segarra, M. L. Buchaillot, J. L. Araus, and S. C. Kefauver, “Remote sensing for precision agriculture: Sentinel- 2 improved features and applications,” May 01, 2020, MDPI AG. doi: 10.3390/agronomy10050641

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S2ToolBox Level 2 products LAI, FAPAR, FCOVER,

M. Weiss, F. Baret, S. Jay, S. S. Jay, T. Level, and products Lai, “S2ToolBox Level 2 products LAI, FAPAR, FCOVER,” 2020. [Online]. Available: https://hal.inrae.fr/hal-03584016v1

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P. Miller, W. Lanier, I. Assistant, S. Brandt, and A. Canada, “MontGuide Using Growing Degree Days to Predict Plant Stages,” 2001. [Online]. Available: www.msuextension.org

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A. Graves, “Long Short -Term Memory,” in Supervised Sequence Labelling with Recurrent Neural Networks , A. Graves, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 37–45. doi: 10.1007/978-3-642-24797-2_4

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G. Mauromicale, A. Lo Monaco, and A. M. G. Longo, “ Effect of Branched Broomrape ( Orobanche ramosa ) Infection on the Growth and Photosynthesis of Tomato ,” Weed Sci, vol. 56, no. 4, pp. 574–581, Aug. 2008, doi: 10.1614/ws-07-147.1

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APPENDIX Appendix Table A1. Spectral Vegetation Indices Used in This Study Index Acronym Full Name NDVI Normalized Difference Vegetation Index ARI Anthocyanin Reflectance Index mARI Modified Anthocyanin Reflectance Index ARVI Atmospherically Resistant Vegetation Index CHL-RED-...

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [1]

The parasite’s life cycle is predominantly subterranean, making early detection challenging

INTRODUCTION Branched broomrape ( Phelipanche ramosa ) is a parasitic plant that poses a significant threat to tomato cultivation by attaching to the host’s roots and siphoning essential nutrients, potentially leading to yield reductions of up to 80% [1]. The parasite’s life cycle is predominantly subterranean, making early detection challenging. Furtherm...

2025

[2] [2]

METHODOLOGY 2.1. INTEGRATED SATELLITE DATA PROCESSING AND FEATURE EXTRACTION FOR PHENOLOGICAL ANALYSIS Five broomrape infested (identified and reported by farmers) tomato fields were compared to five non -infested fields in the same region. For each field, Sentinel imagery across the tomato growing season were monitored, capturing key phenological stages ...

[3] [3]

RESULTS AND DISCUSSION The LSTM model was trained for 100 epochs using five-fold cross-validation, with the dataset split into 65% for training, 15% for validation, and 30% for testing. As shown in Figures 3a and 3b, both training and validation accuracy steadily increased, reaching a final training accuracy of 88%, while the corresponding loss curves dec...

[4] [4]

CONCLUSION This study demonstrates a scalable approach for detection of branched broomrape infestation in tomato farms using Sentinel-2 satellite imagery and time -series analysis. A LSTM model, trained on time -seriies spectral feature, achieved promissing performance (88% training accuracy and 87% test accuracy, with precision, recall, and F1 scores of ...

[5] [5]

O. A. Osipitan, B. D. Hanson, and Y. Goldwasser, “UC Agriculture & Natural Resources California Agriculture Title The potential threat of branched broomrape for California processing tomato: A review Permalink Publication Date Copyright Information,” 2021, doi: 10.3733/ca2021a0012

work page doi:10.3733/ca2021a0012 2021

[6] [6]

Using the possibilities of some trap, catch and Brassicaceaen crops for controlling crenate broomrape a problem in lentil fields,

E. Aksoy, Z. F. Arslan, Ö. Tetik, and S. Eymirli, “Using the possibilities of some trap, catch and Brassicaceaen crops for controlling crenate broomrape a problem in lentil fields,” Online, 2016. [Online]. Available: www.ijpp.info

2016

[7] [7]

Technologies for Smart Chemical Control of Broomrape ( Orobanche spp. and Phelipanche spp.) ,

H. Eizenberg, R. Aly, and Y. Cohen, “ Technologies for Smart Chemical Control of Broomrape ( Orobanche spp. and Phelipanche spp.) ,” Weed Sci, vol. 60, no. 2, pp. 316–323, Jun. 2012, doi: 10.1614/ws-d-11-00120.1

work page doi:10.1614/ws-d-11-00120.1 2012

[8] [8]

Leveraging time -based spectral data from UAV imagery for enhanced detection of broomrape in sunflower,

G. Atsmon, A. Brook, T. A. Cohen, F. Kizel, H. Eizenberg, and R. N. Lati, “Leveraging time -based spectral data from UAV imagery for enhanced detection of broomrape in sunflower,” Smart Agricultural Technology, vol. 10, Mar. 2025, doi: 10.1016/j.atech.2025.100809

arXiv 2025