Exploration of Foods and Foodomics

From: Unified CNN-based recognition of multi-crop leaf diseases with web deployment

Table 4. Comparison of different methods for crop leaf disease recognition.

Work done	Problem solved	Key characteristics	Model name	Dataset size	Classes	Accuracy
This work	Multi-crop disease classification (corn, potato, rice, tomato)	Balanced dataset; foliar-specific CNN; class-balanced learning; unified training	Custom CNN	21,000 images	14	99%
Pan et al. [4]	Rice leaf disease detection (fresh/half/spoiled)	YOLOv8-based tiny detector; SENet attention	SSD-YOLO	3,000 images	3	95.3% (mAP)
Saleem et al. [5]	Multi-crop Disease classification (potato, tomato, mango)	NASNetMobile backbone; vegetation index preprocessing; feature fusion; few-shot learning	AgirLeafNet	∼17,000 images	∼15	98% (3-class accuracy)
Bouacida et al. [7]	Cross-crop classification (generalized lesions)	Compact inception-like CNN; focuses on lesion regions; cross-crop generalization	Lesion- Focused CNN	54,000 images	38	94.0% (lab); 97.1% (unseen)
Rajagopal et al. [14]	Classical pipeline (segmentation + SVM)	Fuzzy C-means segmentation; handcrafted vein/texture features; multi-kernel SVM	LK-PSVM	Small lab set	3	96.1%

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CNN: convolutional neural network; SVM: support vector machine.

Declarations

Author contributions

ZWB: Conceptualization, Methodology, Software, Investigation, Data curation, Formal analysis, Visualization, Resources, Writing—original draft, Writing—review & editing. ZBMA: Writing—original draft. MH: Supervision, Conceptualization, Resources, Writing—review & editing. MTH: Conceptualization, Methodology, Supervision, Resources, Project administration, Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

All images used in this study were obtained from publicly available repositories, primarily the rice leaf diseases dataset (Antony and Prasanth [1]), the seasonal corn leaf disease dataset (Ahmad [2]), and the potatof leaf disease dataset in uncontrolled environments (Shabrina et al. [3]), all hosted on Mendeley Data. These images were re-curated into a balanced 14-class dataset of 21,000 images covering corn, potato, rice, and tomato, with fixed training, validation, and test splits, as described in the Materials and methods section. No new raw data were generated in this study, and all source data can be accessed directly from the repositories cited above.

Funding

Not applicable.

Copyright

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Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

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Ahmad H. Seasonal Corn Leaf Disease Dataset: A Multi-Year Collection for Robust Analysis [Internet]. [cited 2025 Jul 21]. Available from: https://doi.org/10.17632/vy629dngm8.1

Shabrina HN, Indarti S, Maharani R, Kristiyanti DA, Irmawati I, Prastomo K. Potato leaf disease dataset in uncontrolled environment [Internet]. [cited 2025 Jul 21]. Available from: https://doi.org/10.17632/ptz377bwb8.1

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