A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture: Privacy, Governance, and Responsible Adoption

Responsible adoption requires that performance be considered together with privacy exposure, governance controls, affected stakeholders, and routes for contesting decisions. This structured evidence review evaluates "A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture" alongside nine author-disjoint, topically matched publications in edge agricultural vision. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through privacy, governance, and responsible adoption, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda combines privacy testing, governance review, subgroup analysis, documentation, and accessible mechanisms for appeal.

1. Zhao, R., Deng, F., Que, H., Liu, M., Yue, X., & Mu, L. (2026). A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture. Sensors, 26(11), 3474. https://doi.org/10.3390/s26113474 DOI
2. Saha, J., Gunasekaran, H., Rajkumar, S., & B, L.-K. (2026). Edge-Ready Lightweight CNN Architectures for Tomato Leaf Disease Detection Using Transfer Learning. . https://doi.org/10.22541/au.176961329.96736990/v1 DOI
3. Salunke, P., & Katti, J. (2025). Tomato Leaf Disease Detection using Lightweight Deep Convolution Network. 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 1236-1243. https://doi.org/10.1109/icaiss61471.2025.11042133 DOI
4. Mostafa, V., Sozol, M.-S.-S., & Mondal, M.-R.-H. (2026). LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection. PLOS One, 21(5), e0349501. https://doi.org/10.1371/journal.pone.0349501 DOI
5. Ibrahim‬‏, F.-S., El-Saber, N.-A.-S., & Mohamed, I.-S. (2026). A Systematic Review of Lightweight and Robust Deep Learning Approaches for Tomato Leaf Disease Detection. Engineering Research Journal (Shoubra), 55(1), 0-0. https://doi.org/10.21608/erjsh.2026.471579.1504 DOI
6. Gookyi, D.-A.-N., Wulnye, F.-A., Wilson, M., Danquah, P., Danso, S.-A., & Gariba, A.-A. (2024). Enabling Intelligence on the Edge: Leveraging Edge Impulse to Deploy Multiple Deep Learning Models on Edge Devices for Tomato Leaf Disease Detection. AgriEngineering, 6(4), 3563-3585. https://doi.org/10.3390/agriengineering6040203 DOI
7. Bellout, A., Zarboubi, M., Elhoseny, M., Dliou, A., Latif, R., & Saddik, A. (2025). LT-YOLOv10n: A lightweight IoT-integrated deep learning model for real-time tomato leaf disease detection and management. Internet of Things, 33, 101663. https://doi.org/10.1016/j.iot.2025.101663 DOI
8. Yang, Z., Dai, B., Wang, R., An, X., Ma, D., & Yun, Y. (2026). Embedded intelligence in agriculture: A lightweight deep learning network for low-cost tomato leaf disease detection. Engineering Applications of Artificial Intelligence, 175, 114612. https://doi.org/10.1016/j.engappai.2026.114612 DOI
9. Lu, J., Liu, Y., Yu, M., & Wei, G. (2026). Lightweight Real-Time Detection Transformer for Tomato Leaf Disease Recognition in Complex Agricultural Scenarios. . https://doi.org/10.21203/rs.3.rs-9832324/v1 DOI
10. Roopasinghe, R.-U.-N., Vasanthapriyan, S., & Chathumini, K.-G.-L. (2026). A Lightweight Incremental Learning Framework for Tomato Leaf Disease Detection in Dynamic Field Deployments. 2026 IEEE International Research Conference on Smart Computing and Systems Engineering (SCSE), 1-6. https://doi.org/10.1109/scse70081.2026.11499804 DOI