Pith. sign in

REVIEW 3 major objections 4 minor 64 references

Leaf diseases detection using deep learning methods

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read The thesis claims that its deep multi-scale convolutional neural network outperforms pre-trained models on ten-class tomato leaf disease classification across accuracy, F1, precision, and recall.

desk verdict A broad, hard-working PhD thesis with a real dataset contribution, but the headline DMCNN result is unverifiable without code/data and a clear statement that test data never informed model selection. read the letter →

arxiv 2501.00669 v1 pith:NDYNPZMB submitted 2024-12-31 cs.LG cs.AIcs.CV

El Houcine El Fatimi This is my paper · ORCID
classification cs.LGcs.AIcs.CV MSC 68T07
keywords deeplearningCNNtomatoleafdiseasemulti-scaleconvolutionalneuralnetworkplantdetectiontransferMobileNetimageclassification
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 thesis tries to establish that a purpose-built deep multi-scale convolutional neural network (DMCNN) can classify tomato leaf diseases more accurately than transferred pre-trained models. The central claim is empirical: on a ten-class tomato leaf image dataset, DMCNN is reported to outperform the state-of-the-art architectures it was compared against on accuracy, F1 score, precision, and recall. The argument is carried by a multi-branch design that reads the same leaf image at several scales and merges the branches into one classifier. If true, the result would give farmers an efficient, custom CNN option for early disease detection and would suggest that multi-scale feature fusion is a better fit for leaf-disease images than single-scale transfer learning.

What carries the argument

The central object is the deep multi-scale CNN (DMCNN): a set of parallel convolutional streams, each operating at a different scale, merged into a single classifier. The multi-branch structure lets the network capture both fine-grained lesion texture and broader leaf context simultaneously, and the fusion layer combines these representations before the final classification. The thesis's claim is that this multi-scale fusion is the mechanism behind the accuracy gain over the pre-trained baselines.

What would settle it

Retrain the DMCNN and the pre-trained baselines on the same ten-class tomato dataset with the test split sealed until the final evaluation, using identical folds; if the accuracy gap shrinks, reverses, or falls within run-to-run variance, the claim that DMCNN outperforms the baselines is not supported.

Watch

Extended reading notes

Core claim

The paper's proposed DMCNN is a multi-branch convolutional architecture in which parallel streams process the leaf image at different scales and are fused at the end into a single output. On a tomato dataset with ten disease classes, the model is reported to achieve higher accuracy, F1, precision, and recall than several pre-trained state-of-the-art architectures, and the thesis presents this as evidence that multi-scale feature fusion captures the lesion-level and leaf-level cues needed for disease classification better than single-scale pre-trained networks.

Load-bearing premise

The accuracy comparisons assume the test images were never used, directly or indirectly, to choose the model or tune its hyperparameters, so the reported margins are unbiased estimates of generalization.

Editorial extensions

If this is right

  • DMCNN classifies ten tomato leaf disease classes with accuracy, F1 score, precision, and recall above the pre-trained models compared in the thesis.
  • Multi-scale feature fusion is a more effective design choice for leaf-disease classification than single-scale transfer learning.
  • A custom CNN trained from scratch can compete with, and in this study beat, large pre-trained networks on a ten-class plant disease dataset.
  • The same architecture and hyperparameter-tuning procedure can be applied to other crops and to real-time disease detection in the field.
  • Dataset composition affects model effectiveness, as demonstrated by the MobileNet experiments on public, collected, and merged bean leaf datasets, so reported gains should be read relative to the dataset used.

Reading between the lines

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

  • If the multi-scale design is the cause of the gain, combining it with transfer learning rather than training from scratch could push accuracy further on smaller datasets.
  • The thesis's MobileNet experiments suggest dataset composition can shift accuracy as much as architecture choice; testing DMCNN on the same public/collected/merged splits would separate architecture effects from data effects.
  • A sealed test set and repeated cross-validation would show whether the reported margin over pre-trained models survives without any tuning on the test data.
  • The seed-image classification work indicates the tuning methodology and possibly the multi-scale architecture could transfer to seed quality sorting and other agricultural image tasks.
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

3 major / 4 minor

Summary. This manuscript, based on a 2023 PhD thesis, applies deep learning to plant leaf disease detection. It contains a broad literature review, experiments with MobileNet on bean leaf datasets, a study of dataset impact, a CNN model for Brassica seeds, and a proposed Deep Multi-Scale CNN (DMCNN) for tomato leaf disease classification. The central empirical claim is that DMCNN outperforms several pre-trained models in accuracy, F1 score, precision, and recall. Evaluation uses public datasets plus a newly collected dataset, and the thesis concludes that the proposed approach yields very satisfactory performance.

Significance. If the evaluation protocol is sound, the claimed DMCNN architecture and its systematic comparison against pre-trained models would be a useful empirical contribution to agricultural computer vision. The paper's strengths include a wide literature review, a detailed account of hyperparameter tuning, the use of GradCAM for qualitative analysis, and the curation of a new leaf disease dataset. However, the significance is currently limited by the absence of statistical uncertainty quantification, the lack of explicit test-set hygiene documentation, and the unavailability of code and data for external verification. The central performance claim is only as strong as the evaluation protocol, and that protocol is not fully described.

major comments (3)
  1. [Abstract; §8.1.2; §8.4.5.2; Table 8.15] The central claim that DMCNN outperforms other models depends on the test set never being used for model or hyperparameter selection. The abstract states that a parameter-tuning algorithm was developed, and Sections 8.1.2.2 and 8.4.5.2 describe tuning learning rate, batch size, and epochs; Table 8.15 documents tuning details for each pre-trained model on the Tomato Leaf dataset. The manuscript never states whether the test split was fixed before tuning or whether model selection used only a validation fold. If the test split informed the final configuration, the reported accuracy advantage is optimistically biased. Please provide the exact split-generation procedure and explicitly confirm that the test set was not used for selection; if nested cross-validation or a validation holdout was used, describe it precisely.
  2. [§8.4.5.3; Tables 8.18–8.20] The comparative tables report single point estimates of accuracy, F1 score, precision, and recall for each architecture. No error bars, confidence intervals, or significance tests are provided, and no repeated runs with different random seeds are reported. Because the reported differences between DMCNN and the baselines could lie within run-to-run variation, the claim of consistent superiority is not statistically supported. Please report means and standard deviations over multiple runs and apply a paired statistical test, such as McNemar's test on the test predictions, to justify the superiority claim.
  3. [§8.4.1.1; §8.4.1.2; §8.4.2] The manuscript does not include a data-availability statement, code, or the exact per-class partition sizes used for the tomato dataset. It also does not report the random seed or the order of augmentation operations. Consequently, the empirical results cannot be reproduced or independently checked, which is especially important for a paper whose contribution is an empirical comparison. Please provide split metadata and per-class counts, and release code or pre-trained weights, or explain clearly why this is not possible.
minor comments (4)
  1. [Throughout] The manuscript contains numerous typographical errors and inconsistent capitalization, such as 'devlop', 'approachs', 'Accuaracy', 'thsis', and 'Mobilenet'. A careful copyedit is needed.
  2. [List of Figures; Figure 8.26] The caption for Figure 8.26 appears duplicated ('Accuracy of loss, validation, and training of the suggested model' is repeated). Please correct the duplicate caption.
  3. [Table 5.1; §5.2.5] The Softmax formula in Table 5.1 is garbled and should be typeset correctly. Reference names are also inconsistent, for example 'Lacun et al., 1998' in Section 5.2.5 versus 'LeCun et al., 1998' elsewhere, and 'Mukti et al., 2013' in the text.
  4. [§8.3; §8.4] The Brassica seeds experiment in Section 8.3 is presented as part of the thesis but is never explicitly connected to the tomato-leaf DMCNN claim in Section 8.4. The relationship between these studies should be clarified, or the seed classification study should be framed as a separate contribution.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the thesis reports empirical comparisons of deep learning models, and the claimed superiority of the proposed DMCNN is not shown to reduce by construction to its inputs.

full rationale

This thesis is primarily an empirical deep-learning study: it collects or reuses leaf-image datasets, trains several CNN-based models, tunes hyperparameters, and reports accuracy, precision, recall, and F1. There is no derivation chain in which a predicted quantity is defined in terms of the very quantity it claims to predict. The proposed DMCNN is described as a multi-branch convolutional architecture and is compared with pre-trained models on a tomato-leaf dataset; this is an external benchmark comparison rather than a self-referential construction. The abstract mentions that a 'parameter-tuning algorithm was developed to identify the optimal performance of each model,' and Section 1 states that the model was fine-tuned by adjusting parameters such as learning rate. This is standard hyperparameter optimization, and the text does not exhibit a specific reduction where the reported test metric is, by construction, the value used to fit the model. A possible risk is that the test split may have informed model selection, but the paper does not state this explicitly, and absence of explicit split metadata is a reproducibility or evaluation-validity concern, not a circularity demonstration under the criteria requiring a quoted equation or fitted parameter renamed as a prediction. The literature review and background chapters are expository and do not smuggle in the thesis's central claim through self-citation. No load-bearing self-citations, no uniqueness theorem imported from the authors' prior work, and no ansatz concealed by citation were found. Therefore the paper's central performance claim has independent empirical content, and no circular step can be exhibited from the provided text.

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

The paper uses standard hyperparameters and a hand-designed architecture. It introduces no new theoretical entities, but the multi-scale design assumption is an ad hoc hypothesis not derived from first principles.

free parameters (5)
  • learning_rate = 0.001, 0.0001, 0.00001
    Learning rate values were compared in Section 8.1.2.2 and the best was chosen based on training/validation accuracy.
  • batch_size = 128, 64, 32
    Batch sizes were compared in Sections 8.1.2.3 and 8.4.5.1 to optimize accuracy and training time.
  • optimizer = Adam, SGD, and others
    Optimizer choice was selected by comparing training accuracy and loss in Section 8.1.2.1.
  • number_of_epochs = not explicitly final, varied
    Epoch count was chosen based on validation accuracy, as shown in Figures 8.25 and 8.37.
  • DMCNN architecture hyperparameters = not fully specified here
    The number of parallel streams, filters, and fusion method in the DMCNN are hand-designed (Section 8.4.3).
assumptions (3)
  • standard math Convolution, pooling, backpropagation, and softmax operate as standard.
    Used throughout Section 5, which is a tutorial on CNNs.
  • domain assumption Leaf images, together with expert labels, are sufficient and accurate for disease classification.
    The evaluation depends on the correctness of public and newly collected datasets (Section 8.1.1.1).
  • ad hoc to paper Merging parallel CNN streams at multiple scales improves leaf disease classification.
    This is the premise of the DMCNN architecture, introduced in Sections 8.4.3 without independent justification.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Leaf diseases detection using deep learning methods." pith.science (2026). https://pith.science/paper/NDYNPZMB

@misc{pith2026250100669,
  author       = {Pith},
  title        = {Pith review of: Leaf diseases detection using deep learning methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NDYNPZMB}},
  note         = {Machine review of arXiv:2501.00669}
}
read the original abstract

This study, our main topic is to devlop a new deep-learning approachs for plant leaf disease identification and detection using leaf image datasets. We also discussed the challenges facing current methods of leaf disease detection and how deep learning may be used to overcome these challenges and enhance the accuracy of disease detection. Therefore, we have proposed a novel method for the detection of various leaf diseases in crops, along with the identification and description of an efficient network architecture that encompasses hyperparameters and optimization methods. The effectiveness of different architectures was compared and evaluated to see the best architecture configuration and to create an effective model that can quickly detect leaf disease. In addition to the work done on pre-trained models, we proposed a new model based on CNN, which provides an efficient method for identifying and detecting plant leaf disease. Furthermore, we evaluated the efficacy of our model and compared the results to those of some pre-trained state-of-the-art architectures.

Figures

Figures reproduced from arXiv: 2501.00669 by the authors.

Figure 4
Figure 4. Relationship between crop types and pathoge [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 6
Figure 6. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 8
Figure 8. [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figures from the paper (28 more)
Figure 8
Figure 8. Figure 8: Performance evaluation of different learning rates: (a) 0.001, [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 8
Figure 8. Figure 8: Exploring batch sizes impact on system [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p110_7.png]
Figure 7
Figure 7. Figure 7: an overview of [PITH_FULL_IMAGE:figures/full_fig_p122_7.png]
Figure 8
Figure 8. Figure 8: illustrates the system components used for [PITH_FULL_IMAGE:figures/full_fig_p144_8.png]
Figure 8.4
Figure 8.4. Figure 8.4: The SGD optimizer achieved the second [PITH_FULL_IMAGE:figures/full_fig_p147_8_4.png]
Figure 8
Figure 8. Figure 8: presents a unique visualization of the training progress, showcasing the [PITH_FULL_IMAGE:figures/full_fig_p156_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p169_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p171_8.png]
Figure 8
Figure 8. Figure 8: M [PITH_FULL_IMAGE:figures/full_fig_p175_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p186_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p196_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p199_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p206_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p210_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p211_8.png]
Figure 8
Figure 8. Figure 8: Class [PITH_FULL_IMAGE:figures/full_fig_p221_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p227_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p229_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p230_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p233_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p235_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p236_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p237_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p240_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p241_8.png]
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p242_8.png]
Figure 8
Figure 8. Figure 8: A comparative analysis with state [PITH_FULL_IMAGE:figures/full_fig_p243_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 49 canonical work pages

  1. [1]

    ToLeD: Tomato leaf disease detection using convolution neural network,

    A. Review of Deep Learning Research https://scite.ai/reports/10.3837/tiis.2019.04.001 https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_h opfield.htm Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. TensorFlowg: a system for fLarge -Scaleg machine learni...

    arXiv 2018

  2. [3]

    A novel multi-head CNN design to identify plant diseases using the fusion of RGB images

    Kaur, M.; Bhatia, R. Development of an improved tomato leaf dis eases detection and classification method. In Proceedings of the IEEE Conference on Information, and Communication Technology Baghdad, Iraq, 15 –16 April 2019; pp. 1 –5. DOI:10.1109/CICT48419.2019.9066230. Kaushik, M.; Ajay, R.; Prakash, P.; Veni, S. Tomato Leaf-Disease Detection using Convol...

    arXiv 2023

  3. [4]

    Recurrent residual unet for medical image segmentation. J. Med. Imag. (Bellingham, Wash.) 6, 014006. Alom, M.Z.; Taha, T.M.; Yakopcic, C.; Westberg, S.; Sidi ke, P.; Nasrin, M.S.; Hasan, M.; Van Essen, B.C.; Awwal, A.A.; Asari, V.K.A state-of-the-art survey on deep learning theory and architectures. Electronics 2019, 8,

    work page 2019

  4. [6]

    Grape crop disease c lassification using transfer learning approach,

    Aravind, K.R., Raja, P., Anirudh, R., Mukesh, K.V., Ashiwin, R., and Vikas Vikas, G., “Grape crop disease c lassification using transfer learning approach,” in Proceedings of the International Conference on ISMAC in Computational Vision and Bio -Engineering 2018 (ISMAC - CVB), vol.30. Springer, Cham. Jan. 2019, pp. 1623 -1633, doi:/10.1007/978-3-030-00665...

    work page doi:10.1007/978-3-030-00665-5150 2022

  5. [8]

    Comparative Study of Deep Learning Software Frameworks

    Segnet: A deep convolutional encoder - decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481–2495. Bahrampour S, Ramakrishnan N, Schott L and Shah M (2015) Comparative study of deep learning software frameworks. arXiv preprint arXiv:1511.06435. Barbedo, J. G. A. (2017). Plant disease diagnosis: Challenges and opport...

    work page Pith review arXiv 2015

  6. [10]

    https://www.mdpi.com/2073-8994/10/1/11

    https://doi.org/10.3390/ sym10010011. https://www.mdpi.com/2073-8994/10/1/11. Liu, B.; Ding, Z.; Tian, L.; He, D.; Li, S.; Wang, H. Grap e Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Front. Plant Sci. 2020, 11,

    work page

  7. [11]

    Identification of apple leaf diseases based on deep convolutional neural networks

    245 Liu, B.; Zhang, Y.; He, D.; Li, Y. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 2017, 10,

    work page 2017

  8. [12]

    Identification of rice plant diseases using lightweight attention networks

    Chen, J.; Zhang, D.; Zeb, A.; Nanehkaran, Y.A . Identification of rice plant diseases using lightweight attention networks. Expert Syst. Appl. 2021, 169, 11451 Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.,

    work page 2021

Show all 64 references
  1. [15]

    A novel approach to classify and detect bean diseases based on image processing,

    An effective algorithm for hyperparameter optimization of neural networks. IBM J. Res. Dev. 61 https://doi.org/10.1147/JRD.2017.2709578. Durmu，s, H.; Gune，s, E.O.; Kırcı, M. Diseases detection on the leaves of the tomato plant by using deep learning. In Proceedings of the 2017...

    2017

  2. [19]

    MIT Press

    Deep Learning. MIT Press. http://www. deeplearningbook.org. Ha, J.G.; M oon, H.; Kwak, J.T.; Hassan, S.I.; Dang, M.; Lee, O.N.; Park, H.Y. Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. J. Appl. Remote Sens. 2017, 11, 0...

    2017

  3. [21]

    MIT Press, Cambridge, MA

    Deep Learning. MIT Press, Cambridge, MA. Indu, V.T.; Priyadharsini, S.S. Crossover -based wind -driven optimized convolutional neural network model for tomato leaf disease classification. J. Plant Dis. Prot. 2021, 129, 559–

    2021

  4. [24]

    https ://doi.org/10.1016/j.asoc.2019.10593

    2019

  5. [25]

    1097–1105

    Harrahs and Harveys, Lake Tahoe, US: Neural Information Processing Systems Foundation, Inc., pp. 1097–1105. Krohling, R.; Esgario, J.; Ventura, J.A. BRACOL —A Brazilian Arabica Coffee Leaf images dataset to identification and quantification of coffee diseases and pests. Mendel...

    2019

  6. [26]

    http://arxiv.org/abs/1901.06032

    A survey of the recent architectures of deep convolutional neural networks, CoRR abs/1901.06032. http://arxiv.org/abs/1901.06032. ArXiv:1901.06032. Kianat, J.; Khan, M.A.; Sharif, M.; Akram, T.; Rehman, A.; Saba, T. A joint framework of feature reduction and robust feature sel...

    1901 arXiv

  7. [31]

    Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks,

    Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007. Liu, B., Zhang, Y., D. He, and Y. Li, “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks,” Symmetry, vol.10, no.1, pp.11, Dec...

    2017 doi

  8. [35]

    v.16, no.83.URL.https://plantmethods

    Liu, J., Wang,X., 2020.Early recognition of tomato gray leaf spot disease based on Mo-bileNetv2- YOLOv3 mode Plant Methods. v.16, no.83.URL.https://plantmethods. biomedcentral.com/articles /10.1186/s13007-020-00624-2. Long, J., Shelhamer, E., Darrell, T.,

    2020 doi

  9. [37]

    classification of tomato leaf diseases using Mobilenetv2,

    M.Z. Siti, M.A.Zulkifley, M. M.Stofa, A.M.Kamari and N.A.Mohamed, “classification of tomato leaf diseases using Mobilenetv2,” Appl. Artif. Intell, vol.9, pp.290 –296, June 2020, doi:110.11591/ijai. v9.i2.pp290- 296 Makerere AI Lab and AIR Lab National Crops Resources Research ...

    2020

  10. [44]

    Deep convolutional neural networks for mobile capture device -based crop disease classification in the wild,

    Pearlstein, L.; Kim, M.; Seto, W. Convolutional neural n etwork application to plant detection, based on synthetic imagery. In Proceedings of the 2016 IEEE Applied Imagery Pattern RecognitionWorkshop (AIPR), Washington, DC, USA, 18–20 October 2016; pp. 1–4. Picon, A., Alvarez-...

    2016

  11. [48]

    In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

    You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788. https://doi.org/10.1109/CVPR.2016.91. Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767 Ren,...

    2016 arXiv

  12. [49]

    Real -time detection of maize crop disease via a dee p learning -based smartphone app,

    Richey, B., Majumder, S., Shirvaikar, M. and N. Kehtarnavaz, “ Real -time detection of maize crop disease via a dee p learning -based smartphone app,” In Real -Time Image Processing and Deep Learning, International Society for Optics and Photonics, Proceedings of the SPIE, vol...

    2020 doi

  13. [50]

    In: Navab, N., Hornegger, J., Wells, W.M

    U -net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M. Rosenfeld, A., & Richardson, A. (2019). Explainability in human –agent systems. Autonomous Agents and Multi-Agent Systems, 33, 673-705. Rumelhart, D.E., Hinton, G.E., Wi...

    2019

  14. [51]

    Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection

    Ruth, J.A.; Uma, R.; Meenakshi, A.; Ramkumar, P. Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection. Neural Process. Lett. 2022.. S Medina, J.R., Garrido, J.M., G´omez -Martín, M.E., Vidal, C.,

    2022

  15. [53]

    Artificial intelligence tools and their capabilities,

    Transfer learning for visual categorization: A survey. IEEE Trans. Neural Netw. Learn. Syst. 26, 1019 –1034. https://doi.org/10.1109/ TNNLS.2014.2330900. Shehu, H. A.; A. Rabie, M. H. Sharif et al., “Artificial intelligence tools and their capabilities,” PLOMS AI, vol. 1, no. 1,

    2014

  16. [56]

    Rethinkin the inception architecture for computer vision,

    Adaptation of convolution and batch normalization layer for cnn implementation on fpga. In: 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream), pp. 1–4. Srinivasan, R., Ramasamy, R., Prabha, A. L., & Prasad, P. V. (2013). Relationship between cro...

    2013

  17. [60]

    Shrivastava, V.K.; Pradhan, M.K.; Minz, S.; Thakur, M.P

    https://doi.org/10.1186/s40537-019-0197-0. Shrivastava, V.K.; Pradhan, M.K.; Minz, S.; Thakur, M.P. Rice plant disease classification using transfer learning of deep convolution neural network. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2019, XLII-3/W6, 631–635. Sibiy...

    2014 arXiv

  18. [61]

    Transfer learning-based deep ensemble neural network for plant leaf disease detection

    Vallabhajosyula, S.; Sistla, V.; Kolli, V.K.K. Transfer learning-based deep ensemble neural network for plant leaf disease detection. J. Plant Dis. Prot. 2021, 129, 545–558. 251 Vijay, N. Detection of Plant Diseases in Tomato Leaves: With Focus on Providing Explainability and ...

    2021

  19. [62]

    Wang, Y.; Zhang, H.; Liu, Q.; Zhang, Y

    ArXiv:1704.03414. Wang, Y.; Zhang, H.; Liu, Q.; Zhang, Y. Image classification of tomato leaf diseases based on transfer learning. J. China Agric. Univ.2019, 24, 124–130. Wei, K.; Chen, B.; Zhang, J.; Fan, S.;Wu, K.; Liu, G.; Chen, D. Explainable Deep Learning Study for Leaf D...

    2019 arXiv

  20. [102]

    Real-Time Blob-Wise Sugar Beets VS Weeds Classification for Monitoring Fields Using Convolutional Neural Networks

    Milioto, A.; Lottes, P.; Stachniss, C. Real-Time Blob-Wise Sugar Beets VS Weeds Classification for Monitoring Fields Using Convolutional Neural Networks. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 4, 41–48. Mingxing Tan, Quoc V. "Le EfficientNet: Rethinking Mode...

    2017 arXiv

  21. [272]

    Disease classification in eggplant using pre-trained vgg16 and msvm

    Rangarajan, A.K.; Purushothaman, R. Disease classification in eggplant using pre-trained vgg16 and msvm. Sci. Rep. 2020, 10, 1–11. Rangarajan, A.K.; Purushothaman, R. Disease Classification in Eggplant Using P re-trained VGG16 and MSVM. Sci. Rep. 2020, 10,

    2020

  22. [343]

    Visual tea leaf disease recognition using a convolutional neural network model

    Chen, J.; Liu, Q.; Gao, L. Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 2019, 11,

    2019

  23. [439]

    Deep residual learning for image recognition,

    Yu, H.; Cheng, X.; Chen, C.; Heidari, A.A.; Liu, J.; Cai, Z.; Chen, H. Apple leaf disease recognition method with improved residual network. Multimed. Tools Appl. 2022, 81, 7759–7782. Zeng, Q.; Ma, X.; Cheng, B.; Zhou, E.; Pang, W. Gans-based data augmentation for citrus disea...

    2022

  24. [578]

    Intan, N.Y.;Naufal A.A.; Akik,H. Mobile Application for Tomato Plant Leaf Disease Detection Using a Dense Convolutional Network Architecture,Computation 2023, 11(2), 20; https://doi.org/10.3390/computation11020020 Ioffe, S.; Szegedy, C. Batch normalization: Accelerating deep n...

    2023 doi

  25. [884]

    Rice blast disease recognition using a deep convolutional neural network,

    Li, Y.; Nie, J.; Chao, X. Do we really need deep CNN for plant diseases identification? Comput. Electron. Agric. 2020, 178, 105803. Liang, W., Zhang, H., Zhang, G. F., and Cao, H.X., “Rice blast disease recognition using a deep convolutional neural network, ”nature.Sci. Rep, v...

    2019 doi

  26. [1035]

    G., Bao, F

    Wu, S. G., Bao, F. S., Xu, E. Y., Wang, Y. -X., Chang, Y.-F., and Xiang,Q.-L. (2007). « A leaf recognition algorithm for plant classification using probabilistic neural network ». In: 2007 IEEE international symposium on signal processing and information technology. IEEE, pp. ...

    2007

  27. [1082]

    Identification of apple leaf diseases based on deep convolutional neural networks

    Liu, B.; Zhang, Y.; He, D.; Li, Y. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 2017, 10,

    2017

  28. [1192]

    University, P

    https://doi.org/10.3390/ agriculture12081192. University, P. S. (2019). PlantVillage. https://plantvillage.psu.edu//.(Visited on 12/10/2019) (cit. on p. 52). URL-1, http://buyukveri.firat.edu.tr/2018/04/17/derin-sinir-aglari-icin aktivasyonfonksiyonlari/, Derin Sinir Ağları İç...

    2019

  29. [1266]

    Plant Disease Detection Using Deep Convolutional Neural Network

    Pandian, J.A.; Kumar, V.D.; Geman, O.; Hnatiuc, M.; Arif, M.; Kanchanadevi, K. Plant Disease Detection Using Deep Convolutional Neural Network. Appl. Sci. 2022, 12,

    2022

  30. [1388]

    Mask R -CNN

    He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R -CNN. In Proceedings of the IEEE International Conference on Computer Vision, ser. ICCV ’17, Venice, Italy, 22 –29 October 2017; pp. 2980–2988. 242 Hebb, D. O. (1949). The organization of behavior; a neuropsychological theo...

    1949 arXiv

  31. [1419]

    Using deep learning for image -based plant disease detection

    Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using deep learning for image -based plant disease detection. Front. Plant Sci. 2016, 7,

    2016

  32. [1741]

    Randaugment: Practical automated data augmentation with a reduced search space

    Cubuk, E.D.; Zoph, B.; Shlens, J.; Le, Q.V. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 14 –19 June 2020; pp. 702–703. Dang, L....

    2009

  33. [1852]

    A mobile -based deep learning model for cassava diseas e diagnosis

    Ramcharan, A.; McCloskey, P.; Baranowski, K.; Mbilinyi, N.; Mrisho, L.; Ndalahwa, M.; Legg, J.; Hughes, D.P. A mobile -based deep learning model for cassava diseas e diagnosis. Front. Plant Sci. 2019, 10,

    2019

  34. [1943]

    A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophys. 115–133. Memon, M.S.; Kumar, P.; Iqbal, R. Meta Deep Learn Leaf Disease Identification Model for Cotton Crop. Computers 2022, 11,

    2022

  35. [1958]

    A robust deep-learning-based detector for real-time tomato plant diseases and pests’ recognition

    Fuentes A, Yoon S, Kim SC, Park DS. A robust deep-learning-based detector for real-time tomato plant diseases and pests’ recognition. Sensors. 2022; 2017:17. Gadekallu, T.R.; Reddy, M.P.K.; Lakshmanna, K.; Rajput, D.S.; Bhattacharya, S.; Jolfaei, A.; Singh, S.; Alazab, M.J.J.o...

    2022 doi

  36. [1992]

    Practical issues in temporal difference learning. Machine Learning

    “Practical issues in temporal difference learning. Machine Learning”, IBM Thomas J. Watson Research Center, pp. 8, 257-277. Thapa, R.; Zhang, K.; Snavely, N.; Belongie, S.; Khan, A. Th e Plant Pathology Challenge 2020 data set to classify foliar disease of apples. Appl. Plant ...

    2020

  37. [1993]

    Transfer learning based plant diseases detection using ResNet50

    A scaled conjugate gradient algorithm for fast supervised learning. Neural networks 6, 525 -533. Neural Network. 6, 525 –533. https://doi.org/10.1016/S0893-6080(05)80056-5. Mosavi A., Varkonyi-Koczy A. R.: Integration of Machine Learning and Optimization for Robot Learning. Ad...

    2017 arXiv

  38. [1998]

    Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324. https://doi.org/10.1109/5.726791. LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L .D. Backpropagation applied to handwritten zip code recognition. Neural ...

    2015 doi

  39. [2004]

    A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks

    Armor damage nalysis using neural networks. Coastal structures 236–248. Menezes Sahu, P., Chug, A., Singh , A.P., Singh,D.,Singh,R.P.,2021. Deep Learning Models for Beans Crop Diseases: Classification and Visualization Techniques, International Journal of Modern Agriculture, v...

    2021

  40. [2009]

    Learning deep architectures for AI. Foundations and trends, in Machine Learning

    “Learning deep architectures for AI. Foundations and trends, in Machine Learning”, Dept. IRO, Universite de Montreal, 2(1): 1-127. Borhani, Y.; Khoramdel, J.; Najafi, E. A d eep learning based approach for automated plant disease classification using vision transformer. Sci. R...

    2016

  41. [2010]

    Systematic literature reviews in software engineering–a tertiary study. Inf. Softw. Technol. 52 (8), 792–805. 244 Krizhevsky A, Sutskever I and Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In Pereira F, Burges CJC, Bottou L and Weinberger K...

    2012

  42. [2014]

    In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp

    Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. Goodfellow, I., Bengio, Y., Courville, A.,

    2014

  43. [2015]

    crop monitoring: Using mobilenet models,

    Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. Lu, J.; Hu, J.; Zhao, G.; Mei, F.; Zhang, C. An In -field Automatic Wheat Disease Diagnosis System. Comput. Electron. Agric. 2017,...

    2018

  44. [2016]

    Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model,

    Canziani, A.; Paszke, A.; Culurciello, E. An analysis of deep neural network models for practical applications. arXiv 2016, arXiv:1605.07678. Channamallikarjuna Mattihalli, Edemialem Gedefaye, Fasil Endalamaw, Adugna Necho., Plant leaf diseases detection and auto-medicine., In...

    2018 arXiv

  45. [2017]

    A deep learning-based approach for banana leaf diseases classification

    Amara, J.; Bouaziz, B.; Algergawy, A. A deep learning-based approach for banana leaf diseases classification. In Proceedings of the Datenbanksysteme für Business, Technologie und Web (BTW 2017)—Workshopband, Stuttgart, Germany, 6–10 March

    2017

  46. [2018]

    Identification of tomato leaf diseases based on combination of ABCK -BWTR and B- ARNet

    Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834–848. Chen, Xiao, Guoxiong Zhou, Aibin Chen, Jizheng Yi, Wenzhuo Z hang, and Yahui Hu. "Identification of tomato leaf...

    2020

  47. [2019]

    Electronics 8.https://www.mdpi.com/2079 - 9292/8/3/292

    A state -of-the-art survey on deep learning theory and architectures. Electronics 8.https://www.mdpi.com/2079 - 9292/8/3/292. doi:10.3390/electronics8030292. Alom, M.Z., Yakopcic, C., Hasan, M., Taha, T.M., Asari, V.K.,

    doi

  48. [2020]

    Retrieved August 15, 2020 from https://d2l.ai/d2l-en.pdf

    Dive into Deep Learning. Retrieved August 15, 2020 from https://d2l.ai/d2l-en.pdf. Atila, Ü.; Uçar, M.; Akyol, K.; Uçar, E. Plant leaf disease classification using EfficientNet deep learning model. Ecol. Inform. 2021, 61, 101182. Badrinarayanan, V., Kendall, A., Cipolla, R.,

    2020

  49. [2021]

    Automatic detection of tomato diseases and pests based on leaf images,

    Shijie,J., Peiyi , J., Siping , H., and Haibo, L., “Automatic detection of tomato diseases and pests based on leaf images,” Chinese Automation Congress,Jinan, China, 2017, doi: 10.1109/CAC.2017.8243388. Shorten, C., Khoshgoftaar, T.M.,

    2017

  50. [2022]

    Identification of plant leaf diseases using a nine-layer deep convolutional neural network

    Gajjar, R.; Gajjar, N.P.; Thakor, V.J.; Patel, N.P.; Ruparelia, S. Real -time detection and identification of plant leaf diseases using convo lutional neural networks on an embedded platform. Vis. Comput. 2022, 38, 2923–2938. Geetharamani, G., and Arun Pandian."Identification ...

    2019

  51. [2322]

    Tomato crop disease classification using pre- trained deep learning algorithm

    Rangarajan, A.K.; Purushothaman, R.; Ramesh, A. Tomato crop disease classification using pre- trained deep learning algorithm. Procedia Comput. Sci. 2018, 133, 1040–1047. [ Ravi, V.; Acharya, V.; Pham, T. D. Attention deep learning-based large-scale learning classifier for Cas...

    2018

  52. [3446]

    Real -time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks

    243 Jiang, P.; Chen, Y.; Liu, B.; He, D.; Liang, C. Real -time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 2019, 7, 69–80. Jin, H.; Li, Y.; Qi, J.; Feng, J.; Tian, D.; Mu,W. GrapeGAN: Unsupervised i...

    2014 arXiv

  53. [5569]

    (2020) Plants disease phenotyping using quinary patterns as texture descriptor

    Ahmad, W., Shah , S., Irtaza, A. (2020) Plants disease phenotyping using quinary patterns as texture descriptor. KSII Trans Internet Inf Syst 14(8):3312–3327 Ai, Y.; Sun, C.; Tie, J.; Cai, X. Research on recognition model of crop diseases and insect pests based on deep learnin...

    2020

  54. [6982]

    Automated leaf disease detection in different crop species through image featu res analysis and One Class Classifiers,

    Pantazi, X.E., Moshou, D., and Tamouridou, A.A., “Automated leaf disease detection in different crop species through image featu res analysis and One Class Classifiers,” Comput. Electron. Agric. vol.156, pp.96 –104, January 2019, doi:/10.1016/j.compag.2018.11.005. Partel, V.; ...

    2019 doi

  55. [7987]

    Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

    https://doi.org/10.3390/s21237987 Tugrul, B.; Elfatimi, E.; Eryigit, R. Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review. Agriculture 2022, 12,

    2022 doi

  56. [8915]

    Identification of disease using deep learning and evaluation of bacteriosis in peach leaf

    Yadav, S.; Sengar, N.; Singh, A.; Singh, A.; Dutta, M.K. Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecol. Inform. 2021, 61, 101247. Yakkundimath, R.; Saunshi, G.; Anami, B.S.; Palaiah, S. Classification of Rice Diseases using Con...

    2021

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.