Exploration of Foods and Foodomics

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

Table 3. Comparison of traditional machine learning models on multi-crop leaf diseases.

Class name	k-NN			SVM			Random Forest
Class name	Precision	Recall	F1 score	Precision	Recall	F1 score	Precision	Recall	F1 score
Corn Cercospora leaf spot	0.7860	0.7889	0.7874	0.8084	0.7815	0.7947	0.8916	0.8222	0.8555
Corn Common rust	1.0000	0.9889	0.9944	1.0000	0.9963	0.9981	0.9926	0.9963	0.9945
Corn northern leaf blight	0.7833	0.8704	0.8246	0.8123	0.8815	0.8455	0.8472	0.9444	0.8932
Corn healthy	0.9011	0.9111	0.9061	0.9321	0.9148	0.9234	0.9007	0.9074	0.9041
Potato early blight	0.9055	0.9222	0.9138	0.9522	0.9593	0.9557	0.9586	0.9444	0.9515
Potato late blight	0.7810	0.7926	0.7868	0.8043	0.8370	0.8203	0.8036	0.8333	0.8182
Potato healthy	0.9130	0.9333	0.9231	0.9434	0.9259	0.9346	0.9432	0.9222	0.9326
Rice bacterial blight	0.9341	0.9444	0.9392	0.9288	0.9667	0.9474	0.9163	0.8926	0.9043
Rice brown spot	0.9213	0.9111	0.9162	0.9185	0.9185	0.9185	0.8648	0.9000	0.8820
Rice leaf smut	0.9361	0.9222	0.9291	0.9547	0.9370	0.9458	0.9225	0.9259	0.9242
Rice healthy	1.0000	0.9593	0.9792	0.9925	0.9778	0.9851	0.9961	0.9444	0.9696
Tomato bacterial spot	0.8889	0.9185	0.9035	0.8815	0.8815	0.8815	0.8869	0.9000	0.8934
Tomato spider mites	0.8876	0.8481	0.8674	0.8759	0.8630	0.8694	0.8776	0.9296	0.9029
Tomato healthy	0.9463	0.8481	0.8945	0.9297	0.8815	0.9049	0.9268	0.8444	0.8837
Overall accuracy	0.8971			0.9087			0.9077

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k-NN: k-nearest neighbors; 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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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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