Edge-optimized multimodal cross-fusion architecture for efficient crop disease detection
Thomas Kinyanjui Njoroge, Kelvin Mugoye Shindu & Rachael Kibuku
Abstract
Accurate and timely crop disease detection is critical for reducing agricultural losses and ensuring food security in low-resource settings. Traditional diagnostic methods, such as manual inspections, are often inefficient and error-prone. Existing deep learning models (e.g., ResNet50, Inception V3) struggle with computational inefficiency and poor generalizability in real-world farming contexts. This study proposes a lightweight multimodal fusion model integrating EfficientNetV2 and MobileNetV2, optimized for edge deployment. The architecture leverages compound scaling and feature fusion to recognize subtle disease patterns, and it was fine-tuned on a globally diverse dataset (PlantVillage and field-collected leaf images). The proposed model achieved state-leading metrics (99.0% accuracy, 0.993 precision, 0.990 F1-score, AUC = 0.999997), outperforming benchmarks like ShuffleNet and DenseNet50 (ranked 2nd–6th). Statistical validation via the Kruskal-Wallis test confirmed significant performance differences across models (H=614.90, p=1.4237e−129), with Bayesian analysis showing a 100% superiority probability over DenseNet50. Notably, the model exhibited the lowest confidence variance (0.000012) compared to alternatives (0.000014–0.000032), demonstrating unmatched prediction stability. Deployment on low-end mobile devices posed challenges such as computational constraints and offline usability. However, the TensorFlow Lite-powered mobile app addressed these limitations, offering real-time, offline disease classification with 0.094-second inference latency on devices with ≤2GB RAM. Validated on 249 unseen field images (95.98% accuracy), this solution bridges the gap between high-performance deep learning and real-world agricultural needs, empowering smallholder farmers with an accessible and scalable tool.
Keywords
crop disease detection, multimodal fusion model, transfer learning, edge computing, EfficientNetV2, MobileNetV2
Author information & Contribution
Thomas Kinyanjui Njoroge. Corresponding author. Department of Computer Science and Informatics, Karatina University, Kenya. Email: tnjoroge@karu.ac.ke
Kelvin Mugoye Shindu. Software Development & Information Systems (SD&IS) Department, School of Technology, KCA University, Kenya.
Rachael Kibuku. Software Development & Information Systems (SD&IS) Department, School of Technology, KCA University, Kenya.
"All authors contributed equally to the conception, design, preparation, data gathering and analysis, and manuscript writing. All authors read and approved the final manuscript."
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was not supported by any funding.
Institutional Review Board Statement
Not applicable.
Data and Materials Availability
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
AI Declaration
AI tools were not used in writing this paper.
Notes
Acknowledgement
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Cite this article:
Njoroge, T.K., Shindu, K.M. & Kibuku, R. (2025). Edge-optimized multimodal cross-fusion architecture for efficient crop disease detection. International Journal of Science, Technology, Engineering and Mathematics, 5(2), 1-37. https://doi.org/10.53378/ijstem.353186
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