Smart Farming 2.0: IoT and Edge AI for Precision Crop Management and Sustainability

Topic Information

Dear Colleagues,

To design and implement an edge AI-enabled IoT framework that enables the precise, real-time monitoring and management of crop health, optimizing resource efficiency and advancing sustainable agricultural practices across diverse environments. 

This interdisciplinary research will accomplish the following objectives:

1. Technology Development

- Create low-cost IoT sensor nodes with edge computing capabilities to collect and process multispectral, soil, and environmental data locally, minimizing cloud dependency. 
- Develop lightweight machine learning models for decentralized anomaly detection, disease prediction, and nutrient deficiency identification.

2. Application and Scalability

- Test the system across varied crops (e.g., cereals, horticulture) and geographies (arid, tropical) to ensure adaptability.
- Integrate with existing farming practices, such as irrigation systems and drone-based monitoring, for seamless adoption.

3. Socioeconomic Impact

- Evaluate reductions in chemical/water usage, yield improvements, and cost savings for small-scale farmers.
- Partner with agricultural cooperatives to assess barriers to technology adoption and co-design user-friendly interfaces.

4. Sustainability Metrics

- Quantify environmental benefits (e.g., carbon footprint reduction, soil health preservation) and align outcomes with UN Sustainable Development Goals (SDGs 2, 12, 13).

Why It Is Novel and Relevant

- Multi-disciplinary fusion: combines agronomy, edge AI, IoT, and socioeconomics to address food security and climate resilience.
- Edge-first innovation: prioritizes decentralized, energy-efficient computing to empower rural and resource-limited settings.
- Real-world impact: bridges the gap between cutting-edge tech and practical farming needs, fostering equitable access to precision agriculture.

Dr. Chiang Liang Kok
Dr. Teck Kheng Lee
Dr. Howard Tang
Prof. Dr. Fanyi Meng
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400
IoT IoT	2.8	8.7	2020	25.5 Days	CHF 1400
Sustainability sustainability	3.3	7.7	2009	17.9 Days	CHF 2400
AgriEngineering agriengineering	3.0	4.7	2019	22 Days	CHF 1800
AI Sensors aisens	-	-	2025	15.0 days *	CHF 1000

* Median value for all MDPI journals in the second half of 2025.

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Published Papers (3 papers)

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All Electronics IoT Sustainability AgriEngineering AI Sensors

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Open AccessSystematic Review

Drone-Based Data Acquisition for Digital Agriculture: A Survey of Wireless Network Applications

by Rogerio Ballestrin, Jean Schmith, Felipe Arnhold, Ivan Müller and Carlos Eduardo Pereira

AgriEngineering 2026, 8(2), 41; https://doi.org/10.3390/agriengineering8020041 - 26 Jan 2026

Viewed by 380

Abstract

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how [...] Read more.

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how drones, acting as mobile data collectors and communication gateways, can enhance the performance of agricultural wireless sensor networks (WSNs). The literature search was carried out in the Scopus and IEEE Xplore databases, considering peer-reviewed studies published in English between 2014 and 2025. After duplicate removal, 985 unique articles were screened based on predefined inclusion and exclusion criteria related to relevance, agricultural application, and communication technologies. Following full-text evaluation, 64 studies were included in this review. The survey analyzes how drones can be efficiently integrated with WSNs to improve data collection, addressing technical and operational challenges such as energy constraints, communication range limitations, propagation losses, and data latency. It further examines the primary applications of drone-based data acquisition supporting efficiency and sustainability in agriculture, identifies the most relevant wireless communication protocols and Technologies and discusses their trade-offs and suitability. Finally, it considers how drone-assisted data collection contributes to improved prediction models and real-time analytics in digital agriculture. The findings reveal persistent challenges in energy management, coverage optimization, and system scalability, but also highlight opportunities for hybrid architectures and the use of intelligent reflecting surfaces (IRSs) to improve connectivity. This work provides a structured overview of current research and future directions in drone-assisted agricultural communication systems. Full article

(This article belongs to the Topic Smart Farming 2.0: IoT and Edge AI for Precision Crop Management and Sustainability)

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20 pages, 9789 KB  

Open AccessArticle

WireDepth: IoT-Enabled Multi-Sensor Depth Monitoring for Precision Subsoiling in Sugarcane

by Saman Abdanan Mehdizadeh, Aghajan Bahadori, Manocheher Ebadian, Mohammad Hasan Sadeghian, Mansour Nasr Esfahani and Yiannis Ampatzidis

IoT 2025, 6(4), 68; https://doi.org/10.3390/iot6040068 - 14 Nov 2025

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Abstract

Subsoil compaction is a major constraint in sugarcane production, limiting yields and reducing resource-use efficiency. This study presents WireDepth, an innovative cloud-connected monitoring system that leverages edge computing and IoT technologies for real-time, spatially aware analysis and visualization of subsoiling depth. The system [...] Read more.

Subsoil compaction is a major constraint in sugarcane production, limiting yields and reducing resource-use efficiency. This study presents WireDepth, an innovative cloud-connected monitoring system that leverages edge computing and IoT technologies for real-time, spatially aware analysis and visualization of subsoiling depth. The system integrates ultrasonic, laser, inclinometer, and potentiometer sensors mounted on the subsoiler, with on-board microcontroller processing and dual wireless connectivity (LoRaWAN and NB-IoT/LTE-M) for robust data transmission. A cloud platform delivers advanced analytics, including 3D depth maps and operational efficiency metrics. System accuracy was assessed using 300 reference depth measurements, with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) calculated per sensor. The inclinometer and potentiometer achieved the highest accuracy (MAPE of 0.92% and 0.84%, respectively), with no significant deviation from field measurements (paired t-tests, p > 0.05). Ultrasonic and laser sensors exhibited higher errors, particularly at shallow depths, due to soil debris interference. Correlation analysis confirmed a significant effect of depth on sensor accuracy, with laser sensors showing the strongest association (Pearson r = 0.457, p < 0.001). Field validation in commercial sugarcane fields demonstrated that WireDepth improves subsoiling precision, reduces energy waste, and supports sustainable production by enhancing soil structure and root development. These findings advance precision agriculture by offering a scalable, real-time solution for subsoiling management, with broad implications for yield improvement in compaction-affected systems. Full article

(This article belongs to the Topic Smart Farming 2.0: IoT and Edge AI for Precision Crop Management and Sustainability)

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18 pages, 5095 KB  

Open AccessArticle

FPGA-Based Low-Power High-Performance CNN Accelerator Integrating DIST for Rice Leaf Disease Classification

by Jingwen Zheng, Zefei Lv, Dayang Li, Chengbo Lu, Yang Zhang, Liangzun Fu, Xiwei Huang, Jiye Huang, Dongmei Chen and Jingcheng Zhang

Electronics 2025, 14(9), 1704; https://doi.org/10.3390/electronics14091704 - 22 Apr 2025

Cited by 5 | Viewed by 3454

Abstract

Agricultural pest and disease monitoring has recently become a crucial aspect of modern agriculture. Toward this end, this study investigates methodologies for implementing low-power, high-performance convolutional neural networks (CNNs) on agricultural edge detection devices. Recognizing the potential of field-programmable gate arrays (FPGAs) to [...] Read more.

Agricultural pest and disease monitoring has recently become a crucial aspect of modern agriculture. Toward this end, this study investigates methodologies for implementing low-power, high-performance convolutional neural networks (CNNs) on agricultural edge detection devices. Recognizing the potential of field-programmable gate arrays (FPGAs) to enhance inference parallelism, we leveraged their computational capabilities and intensive storage to propose an embedded FPGA-based CNN accelerator design aimed at optimizing rice leaf disease image classification. Additionally, we trained the MobileNetV2 network using multimodal image data and employed knowledge distillation from a stronger teacher (DIST) as the hardware benchmark. The solution was deployed on the ZYNQ-AC7Z020 hardware platform using High-Level Synthesis (HLS) design tools. Through a combination of fine-grained pipelining, matrix blocking, and linear buffering optimizations, the proposed system achieved a power consumption of 3.21 W, an accuracy of 97.41%, and an inference speed of 43 ms per frame, making it a practical solution for edge-based rice leaf disease classification. Full article

(This article belongs to the Topic Smart Farming 2.0: IoT and Edge AI for Precision Crop Management and Sustainability)

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