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

Riya Samanta; Bidyut Saha

cs.ETMar 16, 2026

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

Authors: Riya Samanta, Bidyut Saha

Organizations: Techno India University, Salt Lake, West Bengal, India · Sister Nivedita University, Newtown, West Bengal, India

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 & tables

Review	Scope	TinyML Focus	Hardware Profiling	Energy/Resource Metrics	Deployment Orientation	Time Range
Ahmed & Shakoor ( 2025 )	IoT, Big Data, AI in agriculture	No	Limited	No	Survey-level	Broad
Gauttam et al. ( 2026a )	Edge computing in smart agriculture	Partial	Partial	No	Architectural	2020–2025
Heydari & Mahmoud ( 2025a )	TinyML applications survey	Yes	General	Limited	Application-level	Broad
Paul et al. ( 2025 )	Deep learning for plant stress detection	No	No	No	Algorithm-centric	Broad
Madiwal et al. ( 2025 )	Edge AI and IoT for crop disease	Partial	Limited	No	Survey-level	Recent
Singh & Gill ( 2023b )	General survey on Edge AI	No	General	No	Taxonomic	Broad

Table 1: Comparative positioning relative to existing reviews

Application	References	Hardware	Model	Optimization	Key Metric	Offline
Plant Disease Detection	Samanta et al. (2025b)	ESP32-CAM	CNN	Quantization	92% Acc.	Yes
	Koli et al. (2025)	Edge Device	DS-CNN	Lightweight Arch.	96% Acc.	Yes
	Gookyi et al. (2024)	Arduino BLE 33	CNN	Augmentation + Tuning	94.6%, 7.6 ms	Yes
	Annadata et al. (2025)	ESP-EYE 32	CNN	Path Optimization	90% Acc.	Yes
	Tao et al. (2025)	LoRa + Edge	CNN	Adaptive Offloading	–	Partial
	Kouzinopoulos and Manna (2025)	STM32U575	YOLOv8n	Pruning + INT8	51.8 mJ/inf.	Yes

Table 2: Application-wise Summary of TinyML and Edge AI Implementations in Precision Agriculture

Figure 1: Visual representation of the proposed system uti- lizing EPS32-CAM, TFT display, and real-time interpreting tomato plant disease classes (Source: Samanta et al. (2025b) ).

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: Tasci (2026) ).

Figure 3: System Prototype: TinyML-Enabled IoT for Sustainable Precision Irrigation (Source: Taueatsoala et al. (2026) ).

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: Soltani et al. (2025) ).

References	Intelligence Distribution	Edge–Cloud Dependency	Communication Layer	Inference / Training Tier
Samanta et al. (2025b)	Fully On-Device	None	Not Specified	On-Device Inference / Cloud Training
Tao et al. (2025)	Edge-Assisted	Optional	LoRa + IoT Network	Hybrid Inference / Cloud Training
Thirumalaiah et al. (2025)	Edge-Coordinated	Optional	LoRa	Edge Inference / Cloud Training
Soltani et al. (2025)	Distributed Collaborative	Required	Wireless Edge Link	Edge Inference / Distributed Training
Bhattacharya and Pandey (2024)	Edge-Assisted	Optional	IoT Network	Node Inference / Cloud Training
Annadata et al. (2025)	Fully On-Device	None	UAV Internal Control	On-Device Inference / Cloud Training

Table 3: System-Level Deployment Patterns in Edge AI and TinyML-Based Precision Agriculture

References	Quant.	Pruning	Model Comp.	Transfer Learning	Split Learning	DVFS	Adaptive Offloading	Lightweight Arch.
Samanta et al. (2025b)	Yes	–	–	–	–	–	–	Yes
Koli et al. (2025)	Yes	–	Yes	–	–	–	–	Yes
Gookyi et al. (2024)	–	–	–	–	–	–	–	Yes
Tao et al. (2025)	–	–	–	–	–	–	Yes	Yes
Kouzinopoulos and Manna (2025)	Yes	Yes	–	–	–	–	–	Yes
Madiwal et al. (2025)	Yes	–	–	–	–	–	–	Yes

Table 4: Optimization Strategies Adopted in TinyML and Edge AI-Based Precision Agriculture Systems

References	Flash / ROM	RAM	Model Size	Latency	Energy / Inference
Gookyi et al. (2024)	344.7 KB	726.6 KB	CNN-MDD	7.6 ms	–
Kouzinopoulos and Manna (2025)	$<$ 100 KB	–	INT8 YOLOv8n	–	51.8 mJ
Hernandez-Hidalgo et al. (2026)	–	–	6.2 KB Model	–	–
Baishya and Dutta (2025)	–	–	99% Size Reduction	–	–
Bhattacharya and Pandey (2024)	–	–	–	–	8.5% Energy $\downarrow$
Soltani et al. (2025)	–	–	–	–	86% Energy $\downarrow$

Table 5: Energy and Resource Profiling of TinyML and Edge AI Systems in Precision Agriculture

Figure 5: Effect of pruning inference complexity, for YOLOv8n, YOLOv10n and YOLOv11n (Source: Kouzinopoulos and Manna (2025) ).

Figure 6: Privacy-preserving layered Edge AI deployment architecture for agriculture.

Panel of Figure 7 — Figure 7: Summary of deployment-related characteristics: (a) hardware platform distribution, (b) optimization strategies adopted, and (c) reporting frequency of resource-related metrics.

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