The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Low-cost Smart Camera System for Water Stress Detection in Crops
Abstract
The availability of easy-to-use, low-cost, and highly scalable tools makes it possible to achieve rapid and widespread adoption of precision agriculture. In this paper we outline the development of a smart camera system to detect drought stress in corn and soybean crops. The system is comprised of a Raspberry Pi Zero W, Raspberry Pi Camera, WittyPi mini, a cooling and solar power system, temperature sensors both inside and outside of the box, and infrared canopy temperature and light sensors. The system was built to collect data in a configurable time frame and has an embedded machine-learning (ML) processing system. The camera was configured using an Internet of Things (IoT) platform to manage the device and send images to the Cloud. One of the challenges for this system was to effectively implement machine learning models on this limited-resource embedded platform. We achieved an accuracy of 74% with the embedded machine learning algorithm when classifying water stress in soybeans.
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“We noted the existence of some research papers dealing with TinyML as the main topic without mentioning this term in the title neither in the keywords [32], [33], [34] .”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [33]
In the citing paper
“The VGG-19 model is used to distinguish picture types and calculate the quantity of water required for irrigation based on the kind of crop planted.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [33]
In the citing paper
“The authors in [33] proposed a system for agroenvironmental management employing moisture sensors and real-time video analysis of soil photographs.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [33]
Citations
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Two Soybean Plant Introductions Display Slow Leaf Wilting and Reduced Yield Loss under Drought
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An IOT Based Agriculture Monitoring System
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Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques
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Automatic irrigation and worm detection for peanut field using Raspberry pi with OpenCV
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- 0, Deploy machine learning models on mobile and iot devices
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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
“title neither in the keywords [31], [32], [33].”
BackgroundHeydari, S., & Mahmoud, Q. H. (2025) · Sensors · doi:10.3390/s25103191 · Cited by 223
Sensor Based Smart Agriculture with IoT Technologies: A Review
Pyingkodi, M., Thenmozhi, K., Nanthini, K., Karthikeyan, M., Palarimath, S., Erajavignesh, V., & Kumar, G. B. A. (2022) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci54379.2022.9741001 · Cited by 60
Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review
Preite, L., Solari, F., & Vignali, G. (2023) · Sustainability · doi:10.3390/su15075975 · Cited by 59
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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT)
Palarimath, S., Maran, P., Thenmozhi, K., Balakumar, C., Sujatha, T., & R, W. B. N. (2024) · doi:10.1109/iccds60734.2024.10560398 · Cited by 7
Intelligent IoT Platform for Agroecology: Testbed
Bouchemal, N., Chollet, N., & Ramdane-Chérif, A. (2024) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan13060083 · Cited by 5
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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
A Multiple Instance Learning Approach to Study Leaf Wilt in Soybean Plants
Banerjee, S., Ramos-Giraldo, P. J., Reberg-Horton, C. S., Mirsky, S. B., Locke, A. M., & Lobaton, E. J. (2025) · Agriculture · doi:10.3390/agriculture15060614 · Cited by 2
Self-citationCrop prevention through machine learning and IOT with cloud security using blockchain
Singh, A., & Musale, V. (2024) · AIP conference proceedings · doi:10.1063/5.0215909 · Cited by 2
Emerging application use cases and future directions
Ghorpade, S. N., & Chougule, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00024-5 · Cited by 1
Characterization of a Multispectral Camera for Abiotic and Biotic Stress Detection in Greenhouse
Zanchin, A., Sozzi, M., Valentini, T., Placentino, L., Marinello, F., Pozzebon, A., & Duso, C. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-30329-6_103 · Cited by 1
Deep Learning-Based Vision System for Water Stress Classification of Lettuce in Pot Cultivation
Flores, E. J. C., Gonzaga, J. A., Augusto, G. L., Chua, J. A. T., & Lim, L. A. G. (2023) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem60674.2023.10589156 · Cited by 1
Weed and Water Stress Detection Using Drone Video
Mohammed, F., Chattergoon, J., Fontelera, R., Mohammed, O. F., & Hosein, P. A. (2023) · Advances in biological sciences research/Advances in Biological Sciences Research · doi:10.2991/978-94-6463-122-7_45
Cropland observatory nodes (CRONOS): Proximal, integrated soil-plant-atmosphere monitoring systems
Diggins, D. C., Patrignani, A., Krueger, E. S., Brown, W. G., & Ochsner, T. E. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100737
Geng, S., zhang, X., lu, Z., lin, L., li, X., Li, F., & meng, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5399761
Varghese, A., Joseph, J., & Thomas, R. (2026) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee67948.2026.11394635
Manimozhian, A., & Chandel, A. K. (2026) · Agricultural Environment and Sustainability · doi:10.1016/j.ages.2026.100014
Babu, D. S., Atucha, A., & Devi, M. J. (2026) · AgriEngineering · doi:10.3390/agriengineering8110452
Metadata
- Authors
- Ramos-Giraldo, P., Reberg-Horton, S. C., Mirsky, S., Lobaton, E., Locke, A. M., Henriquez, E., Zuniga, A., & Minin, A.
- Affiliations
- North Carolina State University (US)
- Agricultural Research Service (US)
- Venue
- 2020 IEEE SENSORS
- Publisher
- IEEE
- Published
- 2020-10-25
- Pages
- 1-4
- ISSN
- 1930-0395 (ISSN-L)
- OpenAlex
- W3112692228
- Cited by
- 24
- References
- 7
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 6 · 2022: 4
- FWCI
- 3.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... (publishedVersion)
- Funders
- National Institute of Food and Agriculture
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Keywords
- drought stress detection, maize, soybean, embedded machine learning, Raspberry Pi, Internet of Things
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