Keywords
Model Compression
Domain Shift
Label Quality
Device Latency
Decision Support
Abstract
Edge Smart Agriculture poses a recurring problem of coordinating performance with evidence quality, resource limits, and transfer across settings. The present synthesis investigates connecting compact visual models with sensing conditions, deployment limits, and agronomic action. The corpus joins 1 focal paper with 11 independently retrieved publications confirmed at bibliographic registration or publisher level. The analysis is organized around model compression, domain shift, label quality, device latency, and decision support. To avoid reading reported outcomes as directly interchangeable, the review compares research framing, method assumptions, and test envelope. Across the literature, the literature consistently implies that advances in edge smart agriculture become credible when representation, objective, and evaluation protocol are evaluated together and when uncertainty about distribution shift is reported explicitly. The synthesis ties method selection to operational consequence while identifying external-validity hazards, and proposes a research agenda centered on well-specified controls, robustness tests, and reproducible workflows.
References
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.
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
Nguyen-Tat, T. B., & Nong, D. T. (2026). Lightweight deep learning framework and real-world dataset for durian leaf disease detection in smart agriculture. Displays, 93, 103451. https://doi.org/10.1016/j.displa.2026.103451
Xu, Y., Liu, Y., Meng, X., Yuan, Q., Wang, D., Wu, L., et al. (2026). Lightweight Visual Detection Framework for Real-Time Rice Leaf Disease Identification on Edge Mobile Robots. Agriculture, 16(13), 1383. https://doi.org/10.3390/agriculture16131383
Liu, X., Teng, W., Yu, H., Yao, Z., Wang, C., Peng, Y., et al. (2025). GAE-YOLO: a lightweight multimodal detection framework for tomato smart agriculture with edge computing. Frontiers in Plant Science, 16. https://doi.org/10.3389/fpls.2025.1712432
Zhang, P., Li, R., Liu, Y., Sun, G., & Wen, C. (2026). I-GhostNetV3: A Lightweight Deep Learning Framework for Vision-Sensor-Based Rice Leaf Disease Detection in Smart Agriculture. Sensors, 26(3), 1025. https://doi.org/10.3390/s26031025
Luo, M. (2025). The application of deep learning technology in smart agriculture: Lightweight apple leaf disease detection model. International Journal for Simulation and Multidisciplinary Design Optimization, 16, 7. https://doi.org/10.1051/smdo/2025006
Wang, J., Li, M., Han, C., & Guo, X. (2024). YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection. Agriculture, 14(8), 1240. https://doi.org/10.3390/agriculture14081240
Hu, Y., Liu, G., Chen, Z., Liu, J., & Guo, J. (2023). Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation. Agriculture, 13(9), 1664. https://doi.org/10.3390/agriculture13091664
Lu, J., & Ahmad, F. K. (2026). SFLD-YOLO: A lightweight strawberry fruit and leaf disease detection method. Smart Agricultural Technology, 14, 102310. https://doi.org/10.1016/j.atech.2026.102310
Si, Q., Song, Y., & Han, S. I. (2026). ShuffleNetV2-hSimKD: A Lightweight Network for Plant Disease Detection. Agriculture, 16(15), 1686. https://doi.org/10.3390/agriculture16151686
Hussein, L. (2025). Attention-Fused Residual Lightweight MobileViT for Plant Leaf Disease Detection in Agriculture. ITM Web of Conferences, 79, 01037. https://doi.org/10.1051/itmconf/20257901037
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2026 Christopher Scott, Justin Coleman, Brian Edwards (Author)