A Lightweight Deep Learning Model for Crop Disease Detection on Mobile Devices

Step 1: Generate synthetic values for soil health, weather conditions, and plant health
Step 2: Assign disease label using the rule plant health <50
Step 3: Split the dataset into training set (80%) and testing set (20%)
Step 4: Train Random Forest, SVM, and CNN models on training data
Step 5: Evaluate models using accuracy, F1-score, and inference time
Step 6: Compare the performance of the models

Table 2:

RF classifier

Class	Precision	Recall	F1-score	Support
0 (No disease)	1.00	1.00	1.00	89
1 (disease)	1.00	1.00	1.00	111
Accuracy			1.00	200
Macro avg	1.00	1.00	1.00	200
Weighted avg	1.00	1.00	1.00	200

[i] RF, random forest.

Table 3:

SVM

Class	Precision	Recall	F1-score	Support
0 (No disease)	1.00	1.00	1.00	89
1 (Disease)	1.00	1.00	1.00	111
Accuracy			1.00	200
Macro avg	1.00	1.00	1.00	200
Weighted avg	1.00	1.00	1.00	200

[i] SVM, support vector machine.

Table 4:

CNN model

Evaluation metric	Value
Loss	0.2279
Accuracy	0.875

[i] CNN, convolutional neural network.

CNN model accuracy and loss. CNN, Convolutional Neural Network.

Table 5:

Comparative study of crop disease detection methods

Study	Model used	Dataset type	Accuracy (%)	Mobile deployment suitability
[35]	Deep CNN	Image-based	96.8	Low
[36]	CNN + Mobile App	Image-based	94.5	Medium
[37]	Enhanced CNN	Image-based	97.2	Medium
Proposed method	Lightweight CNN	Simulation-based	87.5	High

[i] CNN, convolutional neural network.

References

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DOI: https://doi.org/10.2478/ijssis-2026-0047 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Jan 2, 2026

Published on: Jul 15, 2026

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

crop disease detection,

deep learning,

simulation-based data,

mobile devices,

machine learning

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2026 Qi Jing, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)

A Lightweight Deep Learning Model for Crop Disease Detection on Mobile Devices

Figures & Tables

Table 1:

Figure 1.

Algorithm 1:

Table 2:

Table 3:

Table 4:

Figure 2:

Figure 3:

Table 5:

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