Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Fine-tuned GPT-4o reaches 98.12% accuracy on apple leaf disease classification, beating ResNet-50 on the same test set.

desk verdict A genuinely useful benchmark report that is transparent about its own limitations, but the headline GPT-4o superiority claim rests on single-split point estimates too small to carry it. read the letter →

arxiv 2504.20419 v1 pith:WJ6YS7NO submitted 2025-04-29 cs.CV

Konstantinos I. Roumeliotis , Ranjan Sapkota , Manoj Karkee , Nikolaos D. Tselikas , Dimitrios K. Nasiopoulos This is my paper · ORCID
classification cs.CV
keywords plantdiseasedetectionmultimodallargelanguagemodelsGPT-4ofew-shotfine-tuningResNet-50zero-shotclassificationimageresolutiongeneralizationprecisionagriculture
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 paper tries to show that a multimodal large language model — GPT-4o, which can look at images as well as read text — can be turned into an accurate plant-disease classifier with only a small amount of labeled leaf images. On a public benchmark of apple and corn leaf images, the authors fine-tuned GPT-4o on 512 images per crop and compared it with a standard convolutional network, ResNet-50. The fine-tuned GPT-4o reached 98.12% accuracy on apple leaves at 256 pixels, ahead of ResNet-50's 96.88%, and its advantage widened at lower resolutions, reaching 12.5 points on corn at 100 pixels. Zero-shot classification was far weaker, so the training matters; cross-plant transfer was poor, meaning the model does not generalize from one crop to another. A sympathetic reader would take the paper's contribution to be evidence that few-shot fine-tuned multimodal LLMs are a viable alternative to CNNs for this task, and that CNN-tuned hyperparameters can be reused to make such tuning affordable.

What carries the argument

The argument runs on three linked mechanisms. First, the dataset is balanced by undersampling each crop to 800 images, split 80/20 with a fixed random state of 42 into 512 training, 128 validation, and 160 test images, with equal class counts in every subset. Second, Bayesian optimization (30 trials with early stopping and pruning) finds ResNet-50's hyperparameters; reusing those same hyperparameters for GPT-4o fine-tuning is what makes the comparison and the cost argument work, because a full GPT-4o hyperparameter search would be prohibitively expensive. Third, the zero-shot and few-shot comparisons use a JSON-formatted prompt that tells the model to output one of four disease labels for the uploaded image. The progressive fine-tuning runs, which add 128 samples at a time, act as a check on whether the transferred hyperparameters or the platform's defaults are responsible for the result, and the paper finds that the transferred hyperparameters give the better accuracy.

What would settle it

Run the same few-shot fine-tuning protocol on multiple random splits while logging which training images the content filter removes each time; if GPT-4o's 98.12% accuracy or its edge over ResNet-50 on the 256-pixel apple test set does not survive across splits, the headline result is tied to one particular split rather than to the model.

Watch

Extended reading notes

Core claim

The central claim is that fine-tuned GPT-4o outperforms ResNet-50 in few-shot plant disease classification across all tested resolutions and both crops, with accuracy up to 98.12% on apple leaves (96.88% for ResNet-50 at 256 pixels) and 93.13% on corn at 100 pixels. The authors also claim that hyperparameters optimized for ResNet-50 via Bayesian search transfer cleanly to GPT-4o fine-tuning; this transferred setting beat the API platform's default settings by up to 2.49% accuracy while training loss reached zero and validation loss stayed low. The paper's own results show the limits: zero-shot GPT-4o reaches only 56.87% on apple and 69.37% on corn, models trained at 100 pixels generalize better to 256-pixel inputs than the reverse, and cross-plant accuracy tops out at 66.87%. During fine-tuning, the API platform's automatic content filter removed 3 to 35 training images per run and flagged different images on repeat runs, which the authors identify as an uncontrolled source of variation in their accuracy numbers.

Load-bearing premise

The comparison assumes that a single fixed 80/20 split — 160 test images per crop — is representative enough that the accuracy gaps between GPT-4o and ResNet-50 reflect genuine model differences, and that the images silently dropped by the automatic content filter did not bias the comparison.

Editorial extensions

If this is right

  • A deployer could classify apple-leaf diseases at near-98% accuracy with 512 labeled images per crop, without collecting large annotation sets.
  • The reusable hyperparameters mean practitioners can tune a small CNN to choose settings for an LLM, avoiding a costly LLM-specific search.
  • Low-resolution training followed by high-resolution inference gave the best cross-resolution results, suggesting that field systems may not need high-resolution sensors to train on.
  • Zero-shot performance is too weak for use without fine-tuning, so an out-of-the-box general-purpose model is not yet a drop-in detector.
  • Cross-plant accuracy stays below 67%, so a model fine-tuned on one crop should not be expected to diagnose another crop's diseases.

Reading between the lines

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

  • Editorial extension: because the paper reports one fixed split, the 1.24-point top-line gap is a point estimate; rerunning across several random splits would show whether GPT-4o's advantage is stable or within run-to-run noise.
  • Editorial extension: the content filter removed different image sets on repeated fine-tuning runs, so even identical submitted jobs may train on different data; logging the IDs of filtered images would let others reproduce or adjust for this.
  • Editorial extension: the prompt asks only for a class label; adding a one-sentence textual description of each disease to the prompt would test whether the model's language knowledge can reduce the number of images needed.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript evaluates GPT-4o against ResNet-50 for four-class plant disease classification on balanced subsets of the PlantVillage dataset (apple and corn, at 100/150/256 px). It reports zero-shot, few-shot fine-tuned, and progressively fine-tuned performance, plus cross-resolution and cross-plant transfer experiments. The central claim is that fine-tuned GPT-4o slightly outperforms ResNet-50, reaching 98.12% accuracy on apple at 256 px versus 96.88% for ResNet-50, with improved generalization and near-zero training loss. The authors also claim that hyperparameters optimized via Bayesian optimization on ResNet-50 transfer effectively to GPT-4o fine-tuning, based on a comparison with a default-hyperparameter progressive training protocol.

Significance. If the central comparative claim were statistically supported, the paper would provide a useful data point on multimodal LLM fine-tuning for plant disease classification, including cost, speed, and resolution-sensitivity trade-offs. It would also offer a practical hyperparameter-transfer recipe (ResNet-50 Bayesian optimization followed by GPT-4o fine-tuning) that could reduce API experimentation costs. The paper is transparent about data-exclusion events caused by OpenAI's content filter, reports monetary costs and runtimes, and makes code/data publicly available. However, as presented, the flagship accuracy gaps are not supported beyond point estimates: the Apple 256 px comparison is 157 versus 155 correct test images out of 160, a difference within sampling noise, and all comparisons rest on a single fixed split with unequal training sets due to non-reproducible content-filter removals. These issues are load-bearing for the abstract's and Section V.A's comparative conclusions.

major comments (4)
  1. [IV.B, Table II; III.B] The headline claim that fine-tuned GPT-4o 'slightly outperforms' ResNet-50 is not statistically supported. With n=160 test images, the approximate standard error of each accuracy is about 1.1–1.4 percentage points, and the Apple 256 px gap of 1.24 points corresponds to only two test images (157 vs. 155 correct). Several other gaps are similarly small in count terms (Apple 150: 5 images; Corn 256: 7 images). All numbers come from a single split with random_state=42; no confidence intervals, standard errors, bootstrap estimates, or repeated-seed evaluations are provided. Because the abstract and Section V.A draw the central comparative conclusion from these point estimates, the paper needs at least a proper uncertainty analysis or multiple-seed evidence before claiming a real difference in accuracy or 'improved generalization.'
  2. [IV.A, IV.C, Table III] The GPT-4o and ResNet-50 training sets were not equal, and the GPT-4o training sets differed across configurations and across repeated runs of the same data. Section IV.A reports that OpenAI's content filter removed 7 apple and 55 corn images from the initial GPT-4o fine-tuning sets; Section IV.C reports different counts for the same datasets (52 apple, 61 corn) in progressive fine-tuning, with per-phase exclusions of 0–43 and 0–14 images. The models are therefore compared after training on different subsets, and the authors themselves attribute the Corn 150 px accuracy drop to the exclusion of 35 images. Under these conditions, a clean model comparison is not possible, and the cross-resolution patterning in Table II may reflect data filtering rather than model capability. The manuscript should either equalize training sets by removing the same flagged images from all models, or present the comparison under matched subsets as a sensitivity analysis.
  3. [IV.C, V.B, Table A1] The claim that ResNet-50-derived hyperparameters 'transfer' to GPT-4o is supported by a comparison that selects the best progressive-fine-tuning phase on the same test set used for evaluation. Table A1 reports accuracy for phases 1–4 at each resolution, and Section V.B then compares the best phase (e.g., Apple 256 phase 3, 95.63%) with the full-training result (98.12%) to conclude a 2.49% advantage for Bayesian-optimized hyperparameters. Choosing the phase with highest test accuracy is a form of test-set model selection, which inflates the apparent gap and invalidates the comparison as an unbiased estimate. Furthermore, the progressive runs use 3-epoch default hyperparameters and only 128-sample increments, so the comparison conflates hyperparameter choice with data quantity and training protocol. Phase selection should be done on validation data, or a nested evaluation should be reported.
  4. [IV.A, V.A, V.D] The claim of 'improved generalization' is not established by the reported evidence. Section IV.A infers generalization from lower training/validation loss in Table I, but near-zero training loss is more naturally read as sign of capacity or memorization, and Table II test accuracies do not systematically align with the validation-loss ordering. The abstract and Section V.A present the generalization claim as a finding, while Section V.D later states that the study 'does not aim to establish whether GPT-4o outperforms other models.' The manuscript should either present test-based evidence for generalization (e.g., held-out robustness metrics, repeated-split generalization intervals) or soften the abstract and conclusion accordingly.
minor comments (6)
  1. [Table I] The table title contains a typo, 'RENNET-50', and the training-duration cell '247.2.0' contains an extra decimal point.
  2. [Table A1] Several F1 entries are shown with three decimals (e.g., 0.956, 0.772, 0.881) while others have four; the table would be clearer with consistent formatting.
  3. [Throughout] Model naming is inconsistent: 'GPT-4o', 'gpt-4o', and 'GPT-Resolution-256-Corn' are used in different places; a single naming convention would improve reproducibility.
  4. [V.D] The sentence in Section V.D that the study does not aim to establish whether GPT-4o outperforms other models directly conflicts with the abstract's comparative claim and with Section V.A's interpretation of the accuracy gaps; this should be reconciled.
  5. [III.A, III.E] The term 'few-shot learning' is used for fine-tuning on 512 labeled images per crop, which is considerably more than typical few-shot conventions; the manuscript should clarify the sample-size terminology or use 'small-sample fine-tuning.'
  6. [References] Some references are duplicated (e.g., [1] and [14] and related entries), and several citation numbers in the text are not in sequential order; a reference-management pass would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: all central results are held-out empirical evaluations.

full rationale

The paper's central claims are empirical rather than derived: zero-shot GPT-4o accuracy (Table IV), fine-tuned GPT-4o accuracy versus ResNet-50 (Table II), and cross-resolution/cross-plant generalization (Tables V and VI) are all evaluated on held-out test images with standard metrics (Eqs. 4-7). The ResNet-50 Bayesian hyperparameter search (Section III.E) is independent of GPT-4o's outputs; GPT-4o is then fine-tuned with those fixed hyperparameters and evaluated on the same fixed test split. The hyperparameter-transfer claim is therefore a genuine out-of-sample test, not a fitted input renamed as a prediction. The progressive fine-tuning comparison (Section IV.C and Table A1) uses OpenAI default hyperparameters as a comparison arm; although confounded by epoch count, batch size, and content-filter removals, it is not circular. Self-citations [87], [100]-[103] appear as background or future-work citations and are not load-bearing for the accuracy comparison. The paper itself flags the content-filter exclusions and single-split design in Sections IV.B and IV.C; these are threats to statistical robustness and reproducibility, not evidence of circular derivation.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

This is an empirical study, so the ledger consists mainly of dataset and modeling assumptions rather than mathematical axioms. The central comparison assumes label correctness, representativeness of the single split, and ignorability of OpenAI's non-random image exclusions.

free parameters (3)
  • ResNet-50 fine-tuning hyperparameters = not reported
    Chosen by Bayesian optimization (Optuna TPE, 30 trials) on validation accuracy; values used for both models are not listed in the paper.
  • Data split random seed = 42
    Single fixed split; no multiple seeds or cross-validation, so variance is unknown.
  • Training epochs and batch size = 10 epochs, batch 16
    Epochs and batch size from ResNet-50 optimization; reused for GPT-4o in the main fine-tuning.
assumptions (4)
  • domain assumption PlantVillage labels are accurate and the four classes per crop are the relevant classes
    Used to define train/test labels in Section III.A-B; any label noise directly affects reported accuracy.
  • domain assumption Images resized to 100/150/256 pixels with PIL thumbnails preserve enough disease information
    Section III.B; the resolution comparison depends on this.
  • ad hoc to paper OpenAI's content-moderation exclusions do not systematically bias the GPT-4o training sets
    Section IV.A-B; the paper itself argues the exclusions may reduce performance, undermining this assumption.
  • ad hoc to paper Hyperparameters optimized on ResNet-50 transfer to GPT-4o
    Core methodological hypothesis stated in Section III.E and tested in Sections IV.A and IV.C.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks." pith.science (2026). https://pith.science/paper/WJ6YS7NO

@misc{pith2026250420419,
  author       = {Pith},
  title        = {Pith review of: Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WJ6YS7NO}},
  note         = {Machine review of arXiv:2504.20419}
}
read the original abstract

Automation in agriculture plays a vital role in addressing challenges related to crop monitoring and disease management, particularly through early detection systems. This study investigates the effectiveness of combining multimodal Large Language Models (LLMs), specifically GPT-4o, with Convolutional Neural Networks (CNNs) for automated plant disease classification using leaf imagery. Leveraging the PlantVillage dataset, we systematically evaluate model performance across zero-shot, few-shot, and progressive fine-tuning scenarios. A comparative analysis between GPT-4o and the widely used ResNet-50 model was conducted across three resolutions (100, 150, and 256 pixels) and two plant species (apple and corn). Results indicate that fine-tuned GPT-4o models achieved slightly better performance compared to the performance of ResNet-50, achieving up to 98.12% classification accuracy on apple leaf images, compared to 96.88% achieved by ResNet-50, with improved generalization and near-zero training loss. However, zero-shot performance of GPT-4o was significantly lower, underscoring the need for minimal training. Additional evaluations on cross-resolution and cross-plant generalization revealed the models' adaptability and limitations when applied to new domains. The findings highlight the promise of integrating multimodal LLMs into automated disease detection pipelines, enhancing the scalability and intelligence of precision agriculture systems while reducing the dependence on large, labeled datasets and high-resolution sensor infrastructure. Large Language Models, Vision Language Models, LLMs and CNNs, Disease Detection with Vision Language Models, VLMs

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Vision Meets Language: A RAG-Augmented YOLOv8 Framework for Coffee Disease Diagnosis and Farmer Assistance

    cs.CV 2025-05 conditional novelty 3.0 of 10

    The paper presents a YOLOv8 + RAG + LLM system for coffee leaf disease detection and remedy suggestions, with detection metrics but no validation of the language output.

Reference graph

Works this paper leans on

102 extracted references · 67 canonical work pages · cited by 1 Pith paper

  1. [2]

    Ratnadass, A., Fernandes, P., Avelino, J., & Habib, R. (2012). Plant species diversity for sustainable management of crop pests and diseases in agroecosystems: a review. Agronomy for sustainable development, 32, 273 - 303

    2012

  2. [4]

    N., Ezugwu, A

    Ngugi, H. N., Ezugwu, A. E., Akinyelu, A. A., & Abualigah, L. (2024). Revolutionizing crop disease detection with computational deep learning: a comprehensive review. Environmental Monitoring and Assessment , 196(3), 302

    2024

  3. [5]

    S., Singh, K

    Upadhyay, A., Chandel, N. S., Singh, K. P., Chakraborty, S. K., Nandede, B. M., Kumar, M., ... & Elbeltagi, A. (2025). Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artificial Intelligence Review, 58(3), 1-64

    2025

  4. [6]

    Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal , 2024(1), 2126734

    2024

  5. [7]

    K., Amirthayogam, G., Sasi, G., Chitra, T., Choubey, A., & Gopalakrishnan, S

    Ruby, E. K., Amirthayogam, G., Sasi, G., Chitra, T., Choubey, A., & Gopalakrishnan, S. (2024). Advanced image processing techniques for automated detection of healthy and infected leaves in agricultural systems. Mesopotamian Journal of Computer Science, 2024, 44-52

    2024

  6. [8]

    Negi, P., & Anand, S. (2024). Plant disease detection, diagnosis, and management: Recent advances and future perspectives. Artificial Intelligence and Smart Agriculture: Technology and Applications, 413-436

    2024

  7. [9]

    A., Kern, I., Pezzuto, F., Lunardi, F., Hofman, P.,

    Calabrese, F., Montero-Fernandez, M. A., Kern, I., Pezzuto, F., Lunardi, F., Hofman, P., ... & Galateau -Salle, F. (2024). The role of pathologists in the diagnosis of occupational lung diseases: an expert opinion of the European Society of Pathology Pulmon ary Pathology Working Group. Virchows Archiv, 485(2), 173-195

    2024

  8. [10]

    D., & Dewasurendra, R

    Karunaweera, N. D., & Dewasurendra, R. (2024). Atypical Leishmania donovani Infections in Sri Lanka: Challenges for Control and Elimination. In Challenges and Solutions Against Visceral Leishmaniasis (pp. 163 -181). Singapore: Springer Nature Singapore

    2024

Show all 102 references
  1. [11]

    M., Ogunbiyi, E

    Kupa, E., Adanma, U. M., Ogunbiyi, E. O., & Solomon, N. O. (2024). Geologic considerations in agrochemical use: impact assessment and guidelines for environmentally safe farming. World Journal of Advanced Research and Reviews, 22, 1761-1771

    2024

  2. [12]

    Zhou, W., Li, M., & Achal, V. (2024). A comprehensive review on environmental and human health impacts of chemical pesticide usage. Emerging Contaminants, 100410

    2024

  3. [13]

    MARKOV, T. (2024). EXTERNAL AND INTERNAL THREATS TO FOOD SECURITY. Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development, 24(2)

    2024

  4. [14]

    B., & Mihretu, F

    Mihrete, T. B., & Mihretu, F. B. (2025). Crop Diversification for Ensuring Sustainable Agriculture, Risk Management and Food Security. Global Challenges, 2400267

    2025

  5. [15]

    Gülmez, B. (2024). Advancements in rice disease detection through convolutional neural networks: A comprehensive review. Heliyon

    2024

  6. [16]

    A., Hassan, R

    Abd-Elsalam, K. A., Hassan, R. K., Ahmed, F. K., & Abdelkhalek, T. E. (2024). Plant Health Check: Emerging Methods for Disease Detection. Plant Quarantine Challenges under Climate Change Anxiety, 79-124

    2024

  7. [17]

    Frem, M., Petrontino, A., Fucilli, V., De Lucia, B., Tria, E., Campobasso, A. A., ... & Bozzo, F. (2024). Consumers’ Perceptions for an Outdoor Ornamental Plant: Exploring the Influence of Novel Plant Diseases Diagnostics and Sustainable Nurseries Cultivation Management. Horti...

    2024

  8. [18]

    Varzakas, T., & Antoniadou, M. (2024). A holistic approach for ethics and sustainability in the food chain: the gateway to oral and systemic health. Foods, 13(8), 1224. [19]Sajitha, P., Andrushia, A. D., Anand, N., & Naser, M. Z. (2024). A review on machine learning and deep l...

    2024

  9. [20]

    Sabrol, H., & Kumar, S. (2015). Recent studies of image and soft computing techniques for plant disease recognition and classification. International Journal of Computer Applications, 126(1)

    2015

  10. [21]

    Vishnu, S., & Ranjith Ram, A. (2015). Plant disease detection using leaf pattern: A review. International Journal of Innovative Science, Engineering & Technology, 2(6), 774-780

    2015

  11. [22]

    & Dandekar, A

    Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., ... & Dandekar, A. M. (2015). Advanced methods of plant disease detection. A review. Agronomy for sustainable development, 35, 1-25

    2015

  12. [23]

    Fang, Y., & Ramasamy, R. P. (2015). Current and prospective methods for plant disease detection. Biosensors, 5(3), 537-561

    2015

  13. [24]

    Wang, H., & Li, H. (2015). Classification recognition of impurities in seed cotton based on local binary pattern and gray level co -occurrence matrix. Transactions of the Chinese Society of Agricultural Engineering, 31(3), 236-241

    2015

  14. [25]

    D., Yakkundimath, R., & Byadgi, A

    Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2013). Classification of fungal disease symptoms affected on cereals using color texture features. International Journal of Signal Processing, Image Processing and Pattern Recognition, 6(6), 321-330

    2013

  15. [26]

    Bankar, S., Dube, A., Kadam, P., & Deokule, S. (2014). Plant disease detection techniques using canny edge detection & color histogram in image processing. Int. J. Comput. Sci. Inf. Technol, 5(2), 1165-1168

    2014

  16. [27]

    (2012, December)

    Revathi, P., & Hemalatha, M. (2012, December). Classification of cotton leaf spot diseases using image processing edge detection techniques. In 2012 International conference on emerging trends in science, engineering and technology (INCOSET) (pp. 169-173). IEEE

    2012

  17. [28]

    (2008, December)

    Weizheng, S., Yachun, W., Zhanliang, C., & Hongda, W. (2008, December). Grading method of leaf spot disease based on image processing. In 2008 international conference on computer science and software engineering (Vol. 6, pp. 491-494). IEEE

    2008

  18. [29]

    Khairnar, K., & Dagade, R. (2014). Disease detection and diagnosis on plant using image processing —a review. International Journal of Computer Applications, 108(13), 36-38

    2014

  19. [30]

    Sankaran, S., Mishra, A., Ehsani, R., & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and electronics in agriculture, 72(1), 1-13

    2010

  20. [31]

    Arnal Barbedo, J. G. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1), 660

    2013

  21. [32]

    Chaerle, L., Hagenbeek, D., De Bruyne, E., Valcke, R., & Van Der Straeten, D. (2004). Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant and Cell Physiology, 45(7), 887-896

    2004

  22. [33]

    Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., & Ramon, H. (2005). Plant disease detection based on data fusion of hyper - spectral and multi -spectral fluorescence imaging using Kohonen maps. Real- Time Imaging, 11(2), 75-83

    2005

  23. [34]

    Ishimwe, R., Abutaleb, K., & Ahmed, F. (2014). Applications of thermal imaging in agriculture—A review. Advances in remote Sensing, 3(3), 128-140

    2014

  24. [35]

    C., & Thai, C

    Yoon, S. C., & Thai, C. N. (2010). Stereo spectral imaging system for plant health characterization. In Technological developments in networking, education and automation (pp. 181-186). Dordrecht: Springer Netherlands

    2010

  25. [36]

    J., & Everitt, J

    Yang, C., Fernandez, C. J., & Everitt, J. H. (2009). Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. In 2009 Reno, Nevada, June 21-June 24, 2009 (p. 1). American Society of Agricultural and Biological Engineers

    2009

  26. [37]

    Terentev, A., Dolzhenko, V., Fedotov, A., & Eremenko, D. (2022). Current state of hyperspectral remote sensing for early plant disease detection: A review. Sensors, 22(3), 757

    2022

  27. [38]

    Demilie, W. B. (2024). Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data, 11(1), 5

    2024

  28. [39]

    O., & Zahid, A

    Ojo, M. O., & Zahid, A. (2023). Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline. Agronomy, 13(3), 887

    2023

  29. [40]

    N., Akinyelu, A

    Ngugi, H. N., Akinyelu, A. A., & Ezugwu, A. E. (2024). Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review. Agronomy, 14(12), 3001

    2024

  30. [41]

    Li, L., Zhang, S., & Wang, B. (2021). Plant disease detection and classification by deep learning—a review. IEEE Access, 9, 56683-56698

    2021

  31. [42]

    M., & Manikandan, V

    Kolli, J., Vamsi, D. M., & Manikandan, V. M. (2021, November). Plant disease detection using convolutional neural network. In 2021 IEEE Bombay Section Signature Conference (IBSSC) (pp. 1-6). IEEE

    2021

  32. [43]

    C., Widodo, A

    Chen, H. C., Widodo, A. M., Wisnujati, A., Rahaman, M., Lin, J. C. W., Chen, L., & Weng, C. E. (2022). AlexNet convolutional neural network for disease detection and classification of tomato leaf. Electronics, 11(6), 951. Under Review in IEEE Transactions 23 | P a g e

    2022

  33. [44]

    Matin, M. M. H., Khatun, A., Moazzam, M. G., & Uddin, M. S. (2020). An efficient disease detection technique of rice leaf using AlexNet. Journal of Computer and Communications, 8(12), 49-57

    2020

  34. [45]

    & Zhu, L

    Li, Z., Li, C., Deng, L., Fan, Y., Xiao, X., Ma, H., ... & Zhu, L. (2022). Improved AlexNet with Inception‐V4 for Plant Disease Diagnosis. Computational intelligence and neuroscience, 2022(1), 5862600

    2022

  35. [46]

    A., Alomani, S

    Alatawi, A. A., Alomani, S. M., Alhawiti, N. I., & Ayaz, M. (2022). Plant disease detection using AI based VGG -16 model. International Journal of Advanced Computer Science and Applications, 13(4)

    2022

  36. [47]

    S., & Malode, V

    Paymode, A. S., & Malode, V. B. (2022). Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artificial Intelligence in Agriculture, 6, 23-33

    2022

  37. [48]

    (2020, June)

    Kumar, A., Razi, R., Singh, A., & Das, H. (2020, June). Res-vgg: A novel model for plant disease detection by fusing vgg16 and resnet models. In International conference on machine learning, image processing, network security and data sciences (pp. 383-400). Singapore: Springe...

    2020

  38. [49]

    Jiangqing, W., Xing, J., Haifang, M., Jun, T., & Chang, L. (2022). Plant disease detection based on lightweight VGG. Journal of Chinese Agricultural Mechanization, 43(4), 25

    2022

  39. [50]

    K., & Krishnamoorthy, R

    Archana, U., Khan, A., Sudarshanam, A., Sathya, C., Koshariya, A. K., & Krishnamoorthy, R. (2023, April). Plant disease detection using resnet. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 614-618). IEEE

    2023

  40. [51]

    (2020, July)

    Kumar, V., Arora, H., & Sisodia, J. (2020, July). Resnet -based approach for detection and classification of plant leaf diseases. In 2020 international conference on electronics and sustainable communication systems (ICESC) (pp. 495-502). IEEE

    2020

  41. [52]

    R., Varma, G

    Reddy, S. R., Varma, G. S., & Davuluri, R. L. (2023). Resnet -based modified red deer optimization with DLCNN classifier for plant disease identification and classification. Computers and Electrical Engineering , 105, 108492

    2023

  42. [53]

    (2020, November)

    Li, X., & Rai, L. (2020, November). Apple leaf disease identification and classification using resnet models. In 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT) (pp. 738- 742). IEEE

    2020

  43. [54]

    S., Sara, K

    Kalaivani, S., Tharini, C., Viswa, T. S., Sara, K. F., & Abinaya, S. T. (2025). ResNet-based classification for leaf disease detection. Journal of The Institution of Engineers (India): Series B, 106(1), 1-14

    2025

  44. [55]

    S., Pawar, P

    Joseph, D. S., Pawar, P. M., & Pramanik, R. (2023). Intelligent plant disease diagnosis using convolutional neural network: a review. Multimedia Tools and Applications, 82(14), 21415-21481

    2023

  45. [56]

    Lu, J., Tan, L., & Jiang, H. (2021). Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture, 11(8), 707

    2021

  46. [57]

    Abbas, A., Jain, S., Gour, M., & Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C -GAN synthetic images. Computers and electronics in agriculture, 187, 106279

    2021

  47. [58]

    Dong, J., Fuentes, A., Zhou, H., Jeong, Y., Yoon, S., & Park, D. S. (2024). The impact of fine -tuning paradigms on unknown plant diseases recognition. Scientific Reports, 14(1), 17900

    2024

  48. [59]

    Zhang, S., & Zhang, C. (2023). Modified U -Net for plant diseased leaf image segmentation. Computers and Electronics in Agriculture, 204, 107511

    2023

  49. [60]

    Liu, W., Yu, L., & Luo, J. (2022). A hybrid attention-enhanced DenseNet neural network model based on improved U -Net for rice leaf disease identification. Frontiers in Plant Science, 13, 922809

    2022

  50. [61]

    Bondre, S., & Patil, D. (2024). Crop disease identification segmentation algorithm based on Mask‐RCNN. Agronomy Journal, 116(3), 1088-1098

    2024

  51. [62]

    U., Khan, M

    Rehman, Z. U., Khan, M. A., Ahmed, F., Damaševičius, R., Naqvi, S. R., Nisar, W., & Javed, K. (2021). Recognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart a griculture. IET Imag...

    2021

  52. [63]

    S., Singh, D., & Rajput, L

    Kumar, M., Chandel, N. S., Singh, D., & Rajput, L. S. (2023). Soybean disease detection and segmentation based on Mask -RCNN algorithm. J Exp Agric Int, 45(5), 63-72

    2023

  53. [64]

    (2022, May)

    Pallapothu, T., Singh, M., Sinha, R., Nangia, H., & Udawant, P. (2022, May). Cotton leaf disease detection using mask RCNN. In AIP Conference Proceedings (Vol. 2393, No. 1). AIP Publishing

    2022

  54. [65]

    (2022, March)

    Kumar, D., & Kukreja, V. (2022, March). Image-based wheat mosaic virus detection with Mask -RCNN model. In 2022 international conference on decision aid sciences and applications (DASA) (pp. 178-182). IEEE

    2022

  55. [66]

    A., & Abd El -Latif, Y

    Gomaa, A. A., & Abd El -Latif, Y. M. (2021). Early prediction of plant diseases using CNN and GANs. International Journal of Advanced Computer Science and Applications, 12(5)

    2021

  56. [67]

    K., Rao, A., Chattopadhyay, P., Maurya, R., & Singh, L

    Singh, A. K., Rao, A., Chattopadhyay, P., Maurya, R., & Singh, L. (2024). Effective plant disease diagnosis using Vision Transformer trained with leafy- generative adversarial network -generated images. Expert Systems with Applications, 254, 124387

    2024

  57. [68]

    Wu, Y., & Xu, L. (2021). Image generation of tomato leaf disease identification based on adversarial-VAE. Agriculture, 11(10), 981

    2021

  58. [69]

    K., & Varanasi, A

    Akkem, Y., Biswas, S. K., & Varanasi, A. (2024). A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network. Engineering Applications of Artificial Intelligence, 131, 107881

    2024

  59. [70]

    (2021, March)

    Mekha, P., & Teeyasuksaet, N. (2021, March). Image classification of rice leaf diseases using random forest algorithm. In 2021 joint international conference on digital arts, media and technology with ECTI northern section conference on electrical, electronics, computer and te...

    2021

  60. [71]

    K., & Pandey, M

    Sahu, S. K., & Pandey, M. (2023). An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model. Expert systems with applications, 214, 118989

    2023

  61. [72]

    (2024, December)

    Wei, T., Chen, Z., & Yu, X. (2024, December). Snap and diagnose: An advanced multimodal retrieval system for identifying plant diseases in the wild. In Proceedings of the 6th ACM International Conference on Multimedia in Asia (pp. 1-3)

    2024

  62. [73]

    Liu, J., & Wang, X. (2024). A multimodal framework for pepper diseases and pests detection. Scientific Reports, 14(1), 28973

    2024

  63. [74]

    (2024, October)

    Wei, T., Chen, Z., Huang, Z., & Yu, X. (2024, October). Benchmarking in-the-wild multimodal disease recognition and a versatile baseline. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 1593-1601)

    2024

  64. [75]

    Lu, Y., Lu, X., Zheng, L., Sun, M., Chen, S., Chen, B., ... & Lv, C. (2024). Application of multimodal transformer model in intelligent agricultural disease detection and question-answering systems. Plants, 13(7), 972

    2024

  65. [76]

    Yan, R., An, P., Meng, X., Li, Y., Li, D., Xu, F., & Dang, D. (2025). A knowledge graph for crop diseases and pests in China. Scientific Data, 12(1), 222

    2025

  66. [77]

    Yang, W., Yang, S., Wang, G., Liu, Y., Lu, J., & Yuan, W. (2023). Knowledge graph construction and representation method for potato diseases and pests. Agronomy, 14(1), 90

    2023

  67. [78]

    GitHub - Applied-AI-Research-Lab/Multimodal-Large-Language- Models-in-Agriculture: Multimodal Large Language Models in Agriculture

    “GitHub - Applied-AI-Research-Lab/Multimodal-Large-Language- Models-in-Agriculture: Multimodal Large Language Models in Agriculture.” Accessed: Mar. 21, 2025. [Online]. Available: https://github.com/Applied-AI- Research-Lab/Multimodal-Large-Language-Models-in-Agriculture

    2025

  68. [79]

    GitHub - spMohanty/PlantVillage-Dataset: Dataset of diseased plant leaf images and corresponding labels

    “GitHub - spMohanty/PlantVillage-Dataset: Dataset of diseased plant leaf images and corresponding labels.” Accessed: Mar. 21, 2025. [Online]. Available: https://github.com/spMohanty/PlantVillage-Dataset

    2025

  69. [80]

    Using deep learning for image-based plant disease detection,

    S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front Plant Sci , vol. 7, no. September, Sep. 2016, doi: 10.3389/FPLS.2016.01419

    2016

  70. [81]

    Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks : Apple Leaves Disease Detection using EfficientNet and DenseNet,

    V. V. Srinidhi, A. Sahay, and K. Deeba, “Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks : Apple Leaves Disease Detection using EfficientNet and DenseNet,” Proceedings - 5th International Conference on Computing Methodologies and Comm...

    2021

  71. [82]

    A classification method for soybean leaf diseases based on an improved ConvNeXt model,

    Q. Wu et al., “A classification method for soybean leaf diseases based on an improved ConvNeXt model,” Scientific Reports 2023 13:1 , vol. 13, no. 1, pp. 1–11, Nov. 2023, doi: 10.1038/s41598-023-46492-3

    2023 doi

  72. [83]

    ViT-SmartAgri: Vision Transformer and Smartphone- Based Plant Disease Detection for Smart Agriculture,

    U. Barman et al., “ViT-SmartAgri: Vision Transformer and Smartphone- Based Plant Disease Detection for Smart Agriculture,” Agronomy 2024, Vol. 14, Page 327 , vol. 14, no. 2, p. 327, Feb. 2024, doi: 10.3390/AGRONOMY14020327

    2024 doi

  73. [84]

    Resnet -based modified red deer optimization with DLCNN classifier for plant disease identification and classification,

    S. R. G. Reddy, G. P. S. Varma, and R. L. Davuluri, “Resnet -based modified red deer optimization with DLCNN classifier for plant disease identification and classification,” Computers and Electrical Engineering , vol. 105, p. 108492, Jan. 2023, doi: 10.1016/J.COMPELECENG.2022.108492

    2023

  74. [85]

    An In -Depth Exploration of ResNet-50 and Transfer Learning in Plant Disease Diagnosis,

    V. S. Desanamukula, T. D. Teja, and P. Rajitha, “An In -Depth Exploration of ResNet-50 and Transfer Learning in Plant Disease Diagnosis,” 7th International Conference on Inventive Computation Technologies, ICICT 2024, pp. 614–621, 2024, doi: 10.1109/ICICT60155.2024.10544802

    2024

  75. [86]

    Introducing vision to the fine-tuning API | OpenAI

    “Introducing vision to the fine-tuning API | OpenAI.” Accessed: Dec. 27,

  76. [87]

    LLMs in e - commerce: A comparative analysis of GPT and LLaMA models in product review evaluation,

    K. I. Roumeliotis, N. D. Tselikas, and D. K. Nasiopoulos, “LLMs in e - commerce: A comparative analysis of GPT and LLaMA models in product review evaluation,” Natural Language Processing Journal , vol. 6, p. 100056, Mar. 2024, doi: 10.1016/J.NLP.2024.100056. Under Review in IE...

    2024

  77. [88]

    Semantic understanding and prompt engineering for large -scale traffic data imputation,

    K. Zhang, F. Zhou, L. Wu, N. Xie, and Z. He, “Semantic understanding and prompt engineering for large -scale traffic data imputation,” Information Fusion, vol. 102, p. 102038, Feb. 2024, doi: 10.1016/J.INFFUS.2023.102038

    2024

  78. [89]

    Automatically generate first draft prompt templates - Anthropic

    Anthropic PBC, “Automatically generate first draft prompt templates - Anthropic.” Accessed: Nov. 30, 2024. [Online]. Available: https://docs.anthropic.com/en/docs/build-with-claude/prompt- engineering/prompt-generator

    2024

  79. [90]

    On Large -Batch Training for Deep Learning: Generalization Gap and Sharp Minima,

    N. S. Keskar, J. Nocedal, P. T. P. Tang, D. Mudigere, and M. Smelyanskiy, “On Large -Batch Training for Deep Learning: Generalization Gap and Sharp Minima,” 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings , Sep. 2016, Accessed...

    2017 arXiv

  80. [91]

    The need for small learning rates on large problems,

    D. R. Wilson and T. R. Martinez, “The need for small learning rates on large problems,” Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 115–119, 2001, doi: 10.1109/IJCNN.2001.939002

    2001

  81. [92]

    Research on Overfitting of Deep Learning,

    H. Li, J. Li, X. Guan, B. Liang, Y. Lai, and X. Luo, “Research on Overfitting of Deep Learning,” Proceedings - 2019 15th International Conference on Computational Intelligence and Security, CIS 2019, pp. 78–81, Dec. 2019, doi: 10.1109/CIS.2019.00025

    2019

  82. [93]

    Enhancing Pneumonia Detection Accuracy Through ResNet -Based Deep Learning Models and Ensemble Techniques: A Study Using Chest X-Ray Images,

    R. Shirvalkar and A. S. Remya Ajai, “Enhancing Pneumonia Detection Accuracy Through ResNet -Based Deep Learning Models and Ensemble Techniques: A Study Using Chest X-Ray Images,” Lecture Notes in Networks and Systems, vol. 949 LNNS, pp. 25–37, 2024, doi: 10.1007/978-981-97-1313- 4_3

    2024 doi

  83. [94]

    A Comparative Analysis of Pretrained Models for Brain Tumaor Classification and Their Optimization Using Optuna,

    K. Hamed and U. Ozgunalp, “A Comparative Analysis of Pretrained Models for Brain Tumaor Classification and Their Optimization Using Optuna,” 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024, 2024, doi: 10.1109/ASYU62119.2024.10757117

    2024

  84. [95]

    A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges,

    M. A. K. Raiaan et al. , “A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges,” IEEE Access, vol. 12, pp. 26839 –26874, 2024, doi: 10.1109/ACCESS.2024.3365742

    2024

  85. [96]

    Innovative deep learning approach for cross -crop plant disease detection: A generalized method for identifying unhealthy leaves,

    I. Bouacida, B. Farou, L. Djakhdjakha, H. Seridi, and M. Kurulay, “Innovative deep learning approach for cross -crop plant disease detection: A generalized method for identifying unhealthy leaves,” Information Processing in Agriculture, vol. 12, no. 1, pp. 54–67, Mar. 2025, do...

    2025 doi

  86. [97]

    Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero -shot Anomaly Detection,

    J. Zhu, S. Cai, F. Deng, and J. Wu, “Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero -shot Anomaly Detection,” Apr. 2024, doi: 10.1145/3664647.3681190

    2024

  87. [98]

    Zero-shot Prompting for LLM-based Machine Translation Using In -domain Target Sentences,

    B. Ji, X. Duan, Y. Zhang, K. Wu, and M. Zhang, “Zero-shot Prompting for LLM-based Machine Translation Using In -domain Target Sentences,” IEEE/ACM Trans Audio Speech Lang Process, 2024, doi: 10.1109/TASLP.2024.3519814

    2024

  88. [99]

    Comparison of Depression Detection Between LLMs and Zero-Shot Learning Using DAD Dataset,

    J. J. D. Leow, H. N. Chua, M. B. Jasser, B. Issa, and R. T. K. Wong, “Comparison of Depression Detection Between LLMs and Zero-Shot Learning Using DAD Dataset,” 2025 21st IEEE International Colloquium on Signal Processing & Its Applications (CSPA) , pp. 295 –300, Feb. 2025...

    2025

  89. [100]

    Think Before You Classify: The Rise of Reasoning Large Language Models for Consumer Complaint Detection and Classification,

    K. I. Roumeliotis, N. D. Tselikas, and D. K. Nasiopoulos, “Think Before You Classify: The Rise of Reasoning Large Language Models for Consumer Complaint Detection and Classification,” Electronics 2025, Vol. 14, Page 1070, vol. 14, no. 6, p. 1070, Mar. 2025, doi: 10.3390/ELECTR...

    2025 doi

  90. [101]

    Object Detection with Multimodal Large Vision-Language Models: An In-depth Review

    Sapkota, R., & Karkee, M. Object Detection with Multimodal Large Vision-Language Models: An In-depth Review. Authorea TechRxiv. April 25,

  91. [102]

    A Review of 3D Object Detection with Vision -Language Models,

    R. Sapkota, K. I. Roumeliotis, R. H. Cheppally, M. Flores Calero, and M. Karkee, “A Review of 3D Object Detection with Vision -Language Models,” arXiv preprint arXiv:2504.18738, 2025. https://doi.org/10.48550/arXiv.2504.18738

    2025 doi arXiv

  92. [103]

    Available at SSRN: https://ssrn.com/abstract=5233953 or http://dx.doi.org/10.2139/ssrn.52 33953 KONSTANTINOS I

    Sapkota, Ranjan and Karkee, Manoj, Object Detection with Multimodal Large Vision -Language Models: An In -Depth Review. Available at SSRN: https://ssrn.com/abstract=5233953 or http://dx.doi.org/10.2139/ssrn.52 33953 KONSTANTINOS I. ROUMELIOTIS is an Adjunct Professor at the De...

    doi

  93. [2024]

    Available: https://openai.com/index/introducing-vision-to-the- fine-tuning-api/

    [Online]. Available: https://openai.com/index/introducing-vision-to-the- fine-tuning-api/

  94. [2025]

    DOI: 10.36227/techrxiv.174559706.69198342/v1

Pith tools

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