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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In the citing paper
“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
Cites
Development of IoT based smart security and monitoring devices for agriculture
Baranwal, T., Nitika, & Pateriya, P. K. (2016) · doi:10.1109/confluence.2016.7508189 · Cited by 238
Differential Wilting among Soybean Genotypes in Response to Water Deficit
King, C. A., Purcell, L. C., & Brye, K. R. (2009) · Crop Science · doi:10.2135/cropsci2008.04.0219 · Cited by 108
Two Soybean Plant Introductions Display Slow Leaf Wilting and Reduced Yield Loss under Drought
Pathan, S. M., Lee, J.-H., Sleper, D. A., Fritschi, F., Sharp, R. E., Carter, T. E., Nelson, R. L., King, C. A., Schapaugh, W. T., Ellersieck, M. R., et al. (2014) · Journal of Agronomy and Crop Science · doi:10.1111/jac.12053 · Cited by 68
An IOT Based Agriculture Monitoring System
Boobalan, J., Jacintha, V. M. E., Nagarajan, J., Thangayogesh, K., & Tamilarasu, S. (2018) · doi:10.1109/iccsp.2018.8524490 · Cited by 50
Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques
Ramos-Giraldo, P. J., Reberg-Horton, C. S., Locke, A. M., Mirsky, S. B., & Lobaton, E. J. (2020) · IT Professional · doi:10.1109/mitp.2020.2986103 · Cited by 46
Self-citationKorodi, A., Anitei, D., Boitor, A., & Silea, I. (2020) · Sensors · doi:10.3390/s20123520 · Cited by 24
Automatic irrigation and worm detection for peanut field using Raspberry pi with OpenCV
Nagarasu, B., & Manimegalai, M. (2016) · Online International Conference on Green Engineering and Technologies · doi:10.1109/get.2016.7916813 · Cited by 5
Research works printed but not linked: 3
- 0, Deploy machine learning models on mobile and iot devices
- 2017, Witty Pi Mini: Realtime Clock and Power Management for Raspberry Pi, User Manual
- abadi, 2015, TensorFlow Large-Scale Machine Learning on Heterogeneous Systems
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
Ask the plants directly: Understanding plant needs using electrical impedance measurements
Garlando, U., Calvo, S., Barezzi, M., Sanginario, A., Ros, P. M., & Demarchi, D. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.106707 · Cited by 51
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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- The paper
- Ramos-Giraldo, P., Reberg-Horton, S. C., Mirsky, S., Lobaton, E., Locke, A. M., Henriquez, E., Zuniga, A., & Minin, A. (2020). Low-cost Smart Camera System for Water Stress Detection in Crops. 2020 IEEE SENSORS. https://doi.org/10.1109/sensors47125.2020.9278744
