Towards lightweight deep neural network for smart agriculture on embedded systems
Abstract
Agriculture is the pillar industry for human survival. However, various diseases threaten the health of crops and lead to a decrease in yield. Industry 4.0 is making strides in plant illness prevention and detection, other than supporting farmers to improve plantations’ income. To prevent crop diseases in time, this paper proposes, implements, and evaluates a low-power smart camera. It features a lightweight neural network to verify and monitor the growth status of crops. The proposed tiny model features optimized complexity, to be deployed in milliwatt power microcontrollers, and high accuracy. Experimental results show that our work reaches 99% accuracy on a 4-classes dataset and more than 96% for a 10 classes dataset. The compact model size (139 kB) and low complexity enable ultra-low power consumption (2.63 mW per hour) on the battery-powered Sony Spresense platform, which features a six-core ARM Cortex-M4F.
Integrity signals
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Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Each IoT device in [127] uses its embedded TinyML model based on Decision Tree to determine the needs of the plants, and then sends this information to the edge node.
“Each IoT device uses its embedded TinyML model based on Decision Tree(DT) to determine the needs of the plants, and then sends this information to the edge node.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [127]
The TinyML model in [127] scored an accuracy of 99%.
“The TinyML model scored an accuracy of 99%.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [127]
Authors in [127] proposed a system where IoT devices and edge nodes work together to monitor the status of plants in a greenhouse to make decisions about the operation of sprinklers.
“The authors in [127] proposed a system where IoT devices and edge nodes work together to monitor the status of the plants in a greenhouse to make decisions about the operation of sprinklers.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [127]
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References 18 · Cited by 5
- Authors
- Du, P., Polonelli, T., Magno, M., & Cheng, Z.
- Affiliations
- Zhejiang University (CN)
- ETH Zurich (CH)
- Venue
- 2022 IEEE Sensors Applications Symposium (SAS)
- Publisher
- IEEE
- Published
- 2022-08-01
- Pages
- 1-6
- OpenAlex
- W4297804416
- Cited by
- 5
- References
- 18
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 2
- FWCI
- 1.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Sensors Applications Symposium (SAS) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Embedded Systems and FPGA Design
- Keywords
- crop disease detection, lightweight neural networks, smart agriculture, embedded systems, ultra-low power, microcontroller, plant growth monitoring, smart cameras, model compression
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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