Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systems

Abstract

Precision agriculture increasingly integrates artificial intelligence to enhance crop monitoring, irrigation management, and resource efficiency. Nevertheless, the vast majority of the current systems are still mostly cloud-based and require reliable connectivity, which hampers the adoption to smaller scale, smallholder farming and underdeveloped country systems. Using recent literature reviews, ranging from 2023 to 2026, this review covers deployments of Edge AI, focused on the evolution and acceptance of Tiny Machine Learning, in low-cost and low-powered agriculture. A hardware-targeted deployment-oriented study has shown pronounced variation in architecture with microcontroller-class platforms i.e. ESP32, STM32, ATMega dominating the inference options, in parallel with single-board computers and UAV-assisted solutions. Quantitative synthesis shows quantization is the dominant optimization strategy; the approach in many works identified: around 50% of such works are quantized, while structured pruning, multi-objective compression and hardware aware neural architecture search are relatively under-researched. Also, resource profiling practices are not uniform: while model size is occasionally reported, explicit flash, RAM, MAC, latency and millijoule level energy metrics are not well documented, hampering reproducibility and cross-system comparison. Moreoever, to bridge the gap between research prototypes and deployment-ready systems, the review also presents a literature-informed deployment perspective in the form of a privacy-preserving layered Edge AI architecture for agriculture, synthesizing the key system-level design insights emerging from the surveyed works. Overall, the findings demonstrate a clear architectural shift toward localized inference with centralized training asymmetry.

Figures (7)

• Figure 1: Visual representation of the proposed system uti- lizing EPS32-CAM, TFT display, and real-time interpreting tomato plant disease classes (Source: Samanta2025).
• Figure 2: Overview of the methodology, illustrating the training phase with DBLA-MobileNetV2 and the deployment phase on Jetson Nano for real-time rice leaf disease classifi- cation (Source: tasci2026dbla).
• Figure 3: System Prototype: TinyML-Enabled IoT for Sustainable Precision Irrigation (Source: TinyMLIrrigation).
• Figure 4: The left sub-figure depicts a single farm with an example of the UAV trajectory for exchanging data with the cluster head devices (red-colored sensors). The right sub-figure illustrates the communication between a cluster head (client/edge device), a UAV, and a server.(Source: Soltani2025).
• Figure 5: Effect of pruning inference complexity, for YOLOv8n, YOLOv10n and YOLOv11n (Source: Kouzinopoulos2025).