Exploration of Foods and Foodomics

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

Table 2. Comparison of deep learning models on multi-crop leaf diseases.

Class name	VGG-16			ResNet-50			Custom CNN
Class name	Precision	Recall	F1 score	Precision	Recall	F1 score	Precision	Recall	F1 score
Corn Cercospora leaf spot	0.9173	0.9037	0.9104	0.9304	0.9407	0.9355	0.9705	0.9741	0.9723
Corn common rust	0.9640	0.9926	0.9781	0.9745	0.9889	0.9816	1.0000	1.0000	1.0000
Corn northern leaf blight	0.9222	0.8778	0.8994	0.9392	0.9148	0.9268	0.9740	0.9704	0.9722
Corn healthy	0.9847	0.9556	0.9699	0.9673	0.9852	0.9761	1.0000	1.0000	1.0000
Potato early blight	0.9288	0.9667	0.9474	0.9780	0.9889	0.9834	0.9818	1.0000	0.9908
Potato late blight	0.8876	0.8778	0.8827	0.9380	0.8963	0.9167	0.9888	0.9815	0.9851
Potato healthy	0.9442	0.9407	0.9425	0.9427	0.9741	0.9581	0.9963	0.9889	0.9926
Rice bacterial blight	0.9962	0.9778	0.9869	0.9851	0.9815	0.9833	1.0000	1.0000	1.0000
Rice brown spot	0.9677	1.0000	0.9836	0.9815	0.9852	0.9834	1.0000	1.0000	1.0000
Rice leaf smut	0.9711	0.9963	0.9835	0.9850	0.9741	0.9795	1.0000	1.0000	1.0000
Rice healthy	1.0000	0.9926	0.9963	1.0000	0.9889	0.9944	1.0000	1.0000	1.0000
Tomato bacterial spot	0.9526	0.9667	0.9596	0.9707	0.9815	0.9761	1.0000	1.0000	1.0000
Tomato spider mites	0.9699	0.9556	0.9627	0.9700	0.9593	0.9646	0.9889	0.9926	0.9908
Tomato healthy	0.9815	0.9852	0.9834	0.9815	0.9852	0.9834	1.0000	0.9926	0.9963
Overall accuracy	0.9563			0.9675			0.9929

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CNN: convolutional neural network.

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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