TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform
Abstract
Smart agriculture researchers bring numerous tools and prospects to the farm ecosystem to improve its productivity and, mainly, its sustainability. Artificial Intelligence (AI) is widely used in precision agriculture as Internet of Things (IoT) technologies have brought a huge volume of data to exploit to provide useful insights for farmers such as weather prediction, pest development detection, or harvest time estimation. AI algorithms are mostly executed in the cloud due to their inherent computing constraints, thus requiring the different sensors to offload their data to the appropriate server. Depending on the amount and volume of data exchanged, the need for computer offloading may induce privacy, security, and latency issues in addition to weighting on the sensor’s battery consumption as wireless transmission methods have a high-energy demand. To overcome this difficulty, recent research has tried to bring AI computation closer to the end device with edge or fog computing and more recently with the Tiny Machine Learning (TinyML) paradigm that aims to embed the AI algorithm directly into the sensor’s microcontroller. In that context, this paper proposes a prototype of smart sensor capable of detecting fruits presence with TinyML. We then study the energy consumption of our system in different IoT scenarios.
Integrity signals
Registry
Cites
Statistics
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?
The final model utilized in the soundscape monitoring system was a CNN based on mel-frequency cepstral coefficients, with an accuracy of 81.6%.
“The final model utilized in the system was a CNN based on mel-frequency cepstral coefficients (MFCCs), with an accuracy of 81.6%.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [94]
The soundscape monitoring system's machine learning algorithms were able to identify noises into four categories: anthrophony, traffic, biophony, and geophony.
“The system's machine learning algorithms were able to identify noises into four categories: anthrophony (human sounds), traffic, biophony (animal sounds), and geophony (environmental sounds).”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [94]
Cites
An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges
Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. H. D. N. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2844296 · Cited by 1,477 · see citing works
Smart farming is key to developing sustainable agriculture
Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707462114 · Cited by 798 · see citing works
Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
Jawad, H. M., Nordin, R., Gharghan, S. K., Jawad, A. M., & Ismail, M. B. (2017) · Sensors · doi:10.3390/s17081781 · Cited by 715 · see citing works
A review on TinyML: State-of-the-art and prospects
Ray, P. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.019 · Cited by 460 · cited 1× · see citing works
“Most research concentrate on the industrial domain, a good survey on recent advancements in Tiny ML has been made by authors in [7].”
BackgroundTinyML-Enabled Frugal Smart Objects: Challenges and Opportunities
Sanchez-Iborra, R., & Skarmeta, A. F. (2020) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2020.3005467 · Cited by 295 · see citing works
Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100273 · Cited by 289 · see citing works
Comparison of the Device Lifetime in Wireless Networks for the Internet of Things
Morin, E., Maman, M., Guizzetti, R., & Duda, A. (2017) · IEEE Access · doi:10.1109/access.2017.2688279 · Cited by 212 · see citing works
Mészáros, L., Varga, A., & Kirsche, M. (2019) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-12842-5_2 · Cited by 80 · see citing works
How to Sustainably Feed 10 Billion People by 2050, in 21 Charts
Ranganathan, J., Waite, R., Searchinger, T., & Hanson, C. (2018) · Cited by 78 · see citing works
Adaptive Traffic Control With TinyML
Roshan, A. N., Gokulapriyan, B., Siddarth, C., & Kokil, P. (2021) · 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet51692.2021.9419472 · Cited by 27 · see citing works
Comparative Study Between MobilNet Face-Mask Detector and YOLOv3 Face-Mask Detector
Aadithya, V., Balakumar, S., Bavishprasath, M., Raghul, M., & Malathi, P. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-6605-6_61 · Cited by 8 · see citing works
Insect biodiversity in agriculture using IoT: opportunities and needs for further research
Zapico, J. L., Ahlgren, F., & Zennaro, M. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682153 · Cited by 3 · see citing works
Flagged first, then by citations
1 of the 13 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey
Mowla, M. N., Mowla, N., Shah, A. F. M. S., Rabie, K. M., & Shongwe, T. (2023) · IEEE Access · doi:10.1109/access.2023.3346299 · Cited by 338 · see citing works
TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
Schizas, N., Karras, A., Karras, C., & Sioutas, S. (2022) · Future Internet · doi:10.3390/fi14120363 · Cited by 236 · see citing works
Tiny Machine Learning: Progress and Futures [Feature]
Lin, J., Zhu, L., Chen, W.-M., Wang, W.-C., & Han, S. (2023) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2023.3302182 · Cited by 216 · see citing works
Senoo, E. E. K., Anggraini, L., Kumi, J. A., Karolina, L. B., Akansah, E., Sulyman, H. A., Mendonça, I., & Aritsugi, M. (2024) · Electronics · doi:10.3390/electronics13101894 · Cited by 154 · see citing works
TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
Karras, A., Γιάνναρος, Α., Karras, C., Theodorakopoulos, L., Mammassis, C. S., Krimpas, G. A., & Sioutas, S. (2024) · Future Internet · doi:10.3390/fi16020042 · Cited by 58 · see citing works
Energy in Smart Cities: Technological Trends and Prospects
Szpilko, D., Fernando, X. N., Nica, E., Budna, K., Rzepka, A., & Lăzăroiu, G. (2024) · Energies · doi:10.3390/en17246439 · Cited by 58 · see citing works
A review of on-device machine learning for IoT: An energy perspective
Tekin, N., Arış, A., Acar, A., Uluagac, A. S., & Güngör, V. Ç. (2023) · Ad Hoc Networks · doi:10.1016/j.adhoc.2023.103348 · Cited by 37 · see citing works
Ding, L., Zhang, C., Yue, Y., Yao, C., Li, Z., Hu, Y., Yang, B., Ma, W., Li, Q., Yu, L., et al. (2025) · Sensors · doi:10.3390/s25144515 · Cited by 37 · see citing works
Smart Farming Technologies: A Methodological Overview and Analysis
Chicaiza, K., Paredes, R. X., Sarzosa, I. M., Yoo, S. G., & Zang, N. (2024) · IEEE Access · doi:10.1109/access.2024.3487497 · Cited by 31 · see citing works
Vergel, Á. J. S., Velez, J. C., Amaya–Mier, R., & Pardo, M. (2023) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2023.104094 · Cited by 28 · see citing works
Energy efficient Firmware Over The Air Update for TinyML models in LoRaWAN agricultural networks
Chollet, N., Bouchemal, N., & Ramdane-Chérif, A. (2022) · International Telecommunication Networks and Applications Conference (ITNAC) · doi:10.1109/itnac55475.2022.9998338 · Cited by 22 · see citing works
Li, T., Luo, J., Liang, K., Yi, C., & Ma, L. (2023) · Sustainability · doi:10.3390/su151813779 · Cited by 22 · see citing works
Empowering Healthcare: TinyML for Precise Lung Disease Classification
Abadade, Y., Benamar, N., Bagaa, M., & Chaoui, H. (2024) · Future Internet · doi:10.3390/fi16110391 · Cited by 21 · see citing works
Shekhar, S., Durgam, M., Khose, S. B., Pohshna, C., & Bhalekar, D. G. (2024) · Springer eBooks · doi:10.1007/978-981-97-5878-4_13 · Cited by 13 · see citing works
Real-Time Anomaly Detection for Industrial Robotic Arms Using Edge Computing
Kayan, H., Heartfield, R., RANA, O. F., Burnap, P., & Perera, C. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3569780 · Cited by 15 · see citing works
Tiny Machine Learning (TinyML): Research trends and future application opportunities
Han, H., Trimi, S., & Lee, S. M. (2026) · Array · doi:10.1016/j.array.2025.100674 · Cited by 12 · see citing works
Fragkou, E., & Katsaros, D. (2024) · Future Internet · doi:10.3390/fi16110413 · Cited by 13 · see citing works
Artificial Intelligence in Smart Agriculture: Applications and Challenges
Nitin, & Gupta, S. B. (2023) · Current Applied Science and Technology · doi:10.55003/cast.2023.254427 · Cited by 11 · see citing works
Time-Series Forecasting to Fill Missing Data in IoT Sensor Data
Rosero-Montalvo, P. D., Tözün, P., & Hernández, W. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3307072 · Cited by 11 · see citing works
TinyML network applications for smart cities
Ahmed, Z. E., Hashim, A. H. A., Saeed, R. A., & Saeed, M. M. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00023-3 · Cited by 9 · see citing works
OBJECT DETECTION ALGORITHMS IMPLEMENTATION ON EMBEDDED DEVICES: CHALLENGES AND SUGGESTED SOLUTIONS
Alaa, R., Hussein, E. A., & Al-Libawy, H. A. (2024) · Kufa Journal of Engineering · doi:10.30572/2018/kje/150309 · Cited by 9 · see citing works
WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications
Balatsouras, C.-P., Karras, A., Karras, C., Karydis, I., & Sioutas, S. (2023) · Sensors · doi:10.3390/s23239486 · Cited by 8 · see citing works
Design and Implementation of a Sustainable Light-based IoT Node on a System-on-Chip
Botirov, K., Perera, A., & Katz, M. D. (2023) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops57953.2023.10283657 · Cited by 7 · see citing works
Sumarsono, Afiatna, F. A. N. F., & Muflihah, N. (2024) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.14.1.18745 · Cited by 7 · see citing works
Industrial Edge MLOps: Overview and Challenges
Rani, F., Chollet, N., Vogt, L., & Urbas, L. (2024) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-443-28824-1.50504-4 · Cited by 6 · see citing works
Embedded AI and Computation Offloading for 6G Green Communication
Chollet, N., Bouchemal, N., & Ramdane-Chérif, A. (2023) · International Conference on 6G Networking (6GNet) · doi:10.1109/6gnet58894.2023.10317778 · Cited by 6 · see citing works
TinyML Anomaly Detection in Portable Cutting Tools
Esmaili, P., Cavedo, F., Esmaılı, P., & Norgia, M. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405679 · Cited by 3 · see citing works
Certainty-Based Neural Network Architecture Selection Framework for TinyML Systems
Komorniczak, J., Puślecki, T., Ksieniewicz, P., & Walkowiak, K. M. (2024) · IEEE Access · doi:10.1109/access.2024.3482111 · Cited by 3 · see citing works
Khan, S. A., Khatri, B., Jonathan, H., Naim, A., & Arora, S. (2025) · Industrial ecology · doi:10.1007/978-981-96-5650-9_11 · Cited by 3 · see citing works
Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
Li, J., Xiao, J., & Yuan, J. (2023) · Sustainability · doi:10.3390/su15065588 · Cited by 3 · see citing works
Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning
Vinu, V. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.57520 · Cited by 2 · see citing works
Arratia, B., Rosas, E., Prades, J., Peña‐Haro, S., Cecilia, J. M., & Manzoni, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112415 · Cited by 2 · see citing works
TinyML for the Detection of Plant Diseases in Resource-Constrained Areas within West Africa
Ibegbu, C., & Korsah, G. A. (2024) · IEEE International Conference on Adaptive Science & Technology · doi:10.1109/icast61769.2024.10856464 · Cited by 1 · see citing works
The future of the artificial intelligence-based agricultural energy internet
Fu, X., Zhao, X., Zhang, J., Qiu, S., & Zhang, X. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27562-3.00008-2 · Cited by 1 · see citing works
Edge intelligence in smart agriculture CPS
Qian, M., Qian, C., & Yu, W. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-326572-3.00019-x · Cited by 1 · see citing works
Edge Computing to Enable Front-End Data Processing Capabilities in Smart Agriculture and Forestry
Tian, Y., Awan, M. F., Jakovljevic, A., Drajic, D. D., Minardi, M., Politis, C., Afhamisis, M., & Palattella, M. R. (2025) · Conference on Cloud and Internet of Things (CIoT) · doi:10.1109/ciot67574.2025.11410015 · Cited by 1 · see citing works
Górecki, S., Hausman, S., & Korbel, P. (2026) · Electronics · doi:10.3390/electronics15174000 · Cited by 1 · see citing works
Shreyas, B., Vasantkumar, C. J., Kudachi, S., & Pattar, V. B. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-22891
Dependability Framework for Smart Tiny Edge Devices
Biswàs, S., & Neogy, S. (2024) · Springer eBooks · doi:10.1007/978-981-97-5157-0_63
Géczy, A., Bojta, P., Straubinger, D., Illés, B., & Harsányi, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24790-3.00003-x
Abdulghafoor, Y. S., Al-Neami, A. Q., & Hussein, A. F. (2025) · Al-Nahrain Journal for Engineering Sciences · doi:10.29194/njes.2801097
A Review on Deep Learning Techniques for Detecting Plant Leaf Diseases
Begum, S., Naresh, E., & Srinidhi, N. N. (2025) · International Conference on Next Generation Communication & Information Processing (INCIP) · doi:10.1109/incip64058.2025.11019376
Vergel, Á. J. S., Ramirez-Ríos, D. G., Velez, J. C., & Amaya–Mier, R. (2025) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2025.102270
Ethical AI in Smart Agriculture 4.0: A Consortium of IoT and AI for Sustainable Farming
Gupta, K., Rajpoot, A. K., & Gupta, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6046-9_36
Automatic Tomato Sorting on Conveyor System Using FOMO Algorithm
Warohma, A. M., Ridwan, A., & Sabriansyah, D. (2025) · International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icast68191.2025.11300198
Localized Operational Intelligence for Smart Farming: A Multi-modal Data Integration Approach
Ilangovan, J., Saraswathi, P. V., & Devaraj, D. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-3978-9_31
Baranidharan, T., Dharun Pradeep, S., H, D. M., & Harish, V. (2026) · International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) · doi:10.1109/etaact69135.2026.11541788
AnomaSense: Intelligent Anomaly Detection through Sensor Data and ML Comparison
Baluchandra, M., & Gayathri, S. (2026) · International Conference on Sustainable Engineering and Technology Innovations (ICSETI) · doi:10.1109/icseti67678.2026.11636495
Öntan, M. S., Dost, F. S., & Işık, A. T. (2026) · European Journal of Geriatrics and Gerontology · doi:10.4274/ejgg.galenos.2026.2026-4-5
Uygun, T., Özgüven, M. M., & Altaş, Z. (2026) · Spanish Journal of Agricultural Research · doi:10.5424/sjar/2026242-21574
Talento, L., Pham, K., & Ramasubramanian, B. (2026) · IEEE Conference on Control Technology and Applications (CCTA) · doi:10.1109/ccta62090.2026.11684216
Kaur, H., Malik, S., Baggan, V., Tyagi, N., & Kumar, D. (2026) · Discover Computing · doi:10.1007/s10791-026-10550-x
- Authors
- Nicolas, C., Naila, B., & Amar, R.-C.
- Affiliations
- ECE, LISV,Paris,France
- ECE,Paris,France
- LISV,Versailles,France
- Venue
- 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN)
- Publisher
- IEEE
- Published
- 2022-07-05
- Pages
- 256-259
- OpenAlex
- W4286248462
- Semantic Scholar
- f1a03d1bba138e166a31155b21e9dfc6b7971619
- Cited by
- 53
- References
- 13
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 15 · 2023: 14 · 2022: 2
- FWCI
- 19.05
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Ubiquitous and Future Networks (publishedVersion)
- HAL (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- TinyML, smart sensors, precision agriculture, Internet of Things, energy conservation, edge computing, microcontroller, fruit detection, energy consumption, on-device AI
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
