Development of a thermal camera for RPAS used to monitor the water status of crops
tatus
DOI:
https://doi.org/10.29312/remexca.v17i4.4097Keywords:
canopy temperature, infrared sensors, RPAS, thermal camera, water stressAbstract
Monitoring and evaluating crop water status is critical to optimizing water use and promoting sustainable agricultural production. The measurement of canopy temperature using thermal images has established itself as a reliable technique for estimating plant water status; however, its adoption is limited by the high cost of thermal cameras and unmanned aerial systems. Due to the above, a low-cost thermal camera was developed based on the 32 × 24 pixel MLX90640 infrared sensor and the Teensy 3.6 microcontroller, with storage capacity on a microSD card. The device was integrated into an unmanned aerial system and evaluated in a corn crop, with its measurements compared with those of a LI-COR LI-1600 porometer (accuracy ±0.5 °C). The thermal images were enhanced by bicubic interpolation and fused with RGB images to obtain images with a resolution of 640 × 480 pixels, which were then processed and segmented using binary images to isolate the pixels corresponding to the crop canopy temperature. Subsequently, the temperature values from the thermal images and the LI-COR LI-1600 porometer at each sampling site were compared, yielding a root mean square error of 0.74 °C. The results show that the developed thermal camera offers adequate accuracy, low cost (135 USD), and high spatial representativeness, positioning itself as a promising tool for thermal canopy monitoring in precision agriculture and efficient water resource management applications.
Downloads
References
Abioye, E. A.; Hensel, O.; Esau, T. J.; Elijah, O.; Abidin, M. S. Z.; Ayobami, A. S.; Yerima, O. and Nasirahmadi, A. 2022. Precision Irrigation management using machine learning and digital farming solutions. AgriEngineering. 4(1)70-103. https://doi.org/10.3390/agriengineering4010006. DOI: https://doi.org/10.3390/agriengineering4010006
Bijanzadeh, E.; Moosavi, S. M. and Bahadori, F. 2022. Quantifying water stress of safflower (Carthamus tinctorius L.) cultivars by crop water stress index under different irrigation regimes. Heliyon. 8(3):1-20. https://doi.org/10.1016/j.heliyon.2022.e09010. DOI: https://doi.org/10.1016/j.heliyon.2022.e09010
Bo, L.; Guan, H.; and Mao, X. 2023. Diagnosing crop water status based on canopy temperature as a function of film mulching and deficit irrigation. Field Crops Research, 304(1):1-10. https://doi.org/10.1016/j.fcr.2023.109154. DOI: https://doi.org/10.1016/j.fcr.2023.109154
Bwambale, E.; Abagale, F. K. and Anornu, G. K. 2022. Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: a review. Agricultural Water Management. 260(1):1-12. Elsevier B. V. https://doi.org/10.1016/j.agwat.2021.107324. DOI: https://doi.org/10.1016/j.agwat.2021.107324
Dong, M.; Shen, H.; Jia, P.; Sun, Y.; Liang, C.; Zhang, F. and Hou, J. 2023. Calibration method for airborne infrared optical systems in a non-thermal equilibrium state. Sensors. 23(14):1-18. https://doi.org/10.3390/s23146326. DOI: https://doi.org/10.3390/s23146326
Elsayed, S.; Elhoweity, M.; Ibrahim, H. H.; Dewir, Y. H.; Migdadi, H. M. and Schmidhalter, U. 2017. Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agricultural Water Management. 189(1):98-110. https://doi.org/10.1016/j.agwat.2017.05.001. DOI: https://doi.org/10.1016/j.agwat.2017.05.001
García-Tejero, I. F.; Gutiérrez-Gordillo, S.; Ortega-Arévalo, C.; Iglesias-Contreras, M.; Moreno, J. M.; Souza-Ferreira, L. and Durán-Zuazo, V. H. 2018. Thermal imaging to monitor the crop-water status in almonds by using the non-water stress baselines. Scientia Horticulturae. 238(1):91-97. https://doi.org/10.1016/j.scienta.2018.04.045. DOI: https://doi.org/10.1016/j.scienta.2018.04.045
Gheysari, M.; Pirnajmedin, F.; Movahedrad, H.; Majidi, M. M. and Zareian, M. J. 2021. Crop yield and irrigation water productivity of silage maize under two water stress strategies in semi-arid environment: two different pot and field experiments. Agricultural Water Management, 255(1):1-9. https://doi.org/10.1016/j.agwat.2021.106999. DOI: https://doi.org/10.1016/j.agwat.2021.106999
Gomes, K. R.; López, D.; Ortega, J. F.; Ballesteros, R.; Poblete, T. y Moreno, M. A. 2017. Calibración de cámaras térmicas no refrigeradas embarcadas en uavs para aplicaciones agronómicas. INOVAGRI International Meeting. 26(4):1-11. https://doi.org/10.7127/iv-inovagri-meeting-2017-res0240343. DOI: https://doi.org/10.7127/iv-inovagri-meeting-2017-res0240343
Jiménez, A. F.; Cárdenas, P. F. and Jiménez, F. 2022. Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture. 192(20):1-19. https://doi.org/10.1016/j.compag.2021.106635. DOI: https://doi.org/10.1016/j.compag.2021.106635
Kamath, R.; Balachandra, M.; Vardhan, A. and Maheshwari, U. 2022. Classification of weeds of paddy fields using deep learning. ECTI Transactions on Computer and Information Technology. 16(4):365-377. https://doi.org/10.37936/ecti-cit.2022164.246857. DOI: https://doi.org/10.37936/ecti-cit.2022164.246857
LI-COR inc. 1989. LI-1600 steady state porometer service manual (issue 82100030). https://licor.app.boxenterprise.net/s/auoxn0ewmmka5r5inwcf.
López-López, R.; Arteaga-Ramírez, R.; Vázquez-Peña, M. A.; López-Cruz, I. y Sánchez-Cohen, I. 2009. Índice de estrés hídrico como un indicador del momento de riego en cultivos agrícolas. Agricultura Técnica en México. 35(1):97-111.
Melexis. 2019. MLX90640 32x24 IR array, Datasheet. https://github.com/melexis/mlx90640-.
Melo, L. L. de; Melo, V. G. M. L.; Marques, P. A. A.; Frizzone, J. A.; Coelho, R. D.; Romero, R. A. F. and Barros, T. H. da S. 2022. Deep learning for identification of water deficits in sugarcane based on thermal images. Agricultural Water Management. 272(1):1-13. https://doi.org/10.1016/j.agwat.2022.107820. DOI: https://doi.org/10.1016/j.agwat.2022.107820
Noguera, M.; Millán, B.; Pérez-Paredes, J. J.; Ponce, J. M.; Aquino, A. and Andújar, J. M. 2020. A new low-cost device based on thermal infrared sensors for olive tree canopy temperature measurement and water status monitoring. Remote Sensing. 12(1):1-20. https://doi.org/10.3390/rs12040723. DOI: https://doi.org/10.3390/rs12040723
Ouma, G.; Wanyama, J.; Kabenge, I.; Jjagwe, J.; Diana, M. and Muyonga, J. 2024. Assessing the effect of deficit drip irrigation regimes on crop performance of eggplant. Scientia Horticulturae, 325(1):1-14. https://doi.org/10.1016/j.scienta.2023.112648. DOI: https://doi.org/10.1016/j.scienta.2023.112648
Oz, N.; Sochen, N.; Mendlovic, D. and Klapp, I. 2025. End-to-end pipeline for simultaneous temperature estimation and super resolution of low-cost uncooled infrared camera frames for precision agriculture applications. Electrical Engineering and Systems Science. 250(1):1-24. http://arxiv.org/abs/2502.13985.
Paciolla, F.; Popeo, G.; Farella, A. and Pascuzzi, S. 2025. Agronomic information extraction from UAV-based thermal photogrammetry using MATLAB. Remote Sensing. 17(1)1-17. https://doi.org/10.3390/rs17152746. DOI: https://doi.org/10.3390/rs17152746
Sagan, V.; Maimaitijiang, M.; Sidike, P.; Eblimit, K.; Peterson, K. T.; Hartling, S.; Esposito, F.; Khanal, K.; Newcomb, M.; Pauli, D.; Ward, R.; Fritschi, F.; Shakoor, N. and Mockler, T. 2019. UAV-based high resolution thermal imaging for vegetation monitoring and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640 and thermomap cameras. Remote Sensing. 11(1)1-29. https://doi.org/10.3390/rs11030330b. DOI: https://doi.org/10.3390/rs11030330
Wu, Y.; Jiang, J.; Zhang, X.; Zhang, J.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W. and Liu, X. 2023. Combining machine learning algorithms and multi-temporal temperature indices to estimate the water status of rice. Agricultural Water Management. 289(1)1-18. https://doi.org/10.1016/j.agwat.2023.108521. DOI: https://doi.org/10.1016/j.agwat.2023.108521
Yun, H.; Lo, S.; Diepenbrock, C. H.; Bailey, B. N.; and Earles, J. M. 2024. VisTA-SR: improving the accuracy and resolution of low-cost thermal imaging cameras for agriculture. Computer Vision. 240(1):1-10. http://arxiv.org/abs/2405.19413.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Revista Mexicana de Ciencias Agrícolas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in Revista Mexicana de Ciencias Agrícolas accept the following conditions:
In accordance with copyright laws, Revista Mexicana de Ciencias Agrícolas recognizes and respects the authors’ moral right and ownership of property rights which will be transferred to the journal for dissemination in open access. Invariably, all the authors have to sign a letter of transfer of property rights and of originality of the article to Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) [National Institute of Forestry, Agricultural and Livestock Research]. The author(s) must pay a fee for the reception of articles before proceeding to editorial review.
All the texts published by Revista Mexicana de Ciencias Agrícolas —with no exception— are distributed under a Creative Commons License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which allows third parties to use the publication as long as the work’s authorship and its first publication in this journal are mentioned.
The author(s) can enter into independent and additional contractual agreements for the nonexclusive distribution of the version of the article published in Revista Mexicana de Ciencias Agrícolas (for example include it into an institutional repository or publish it in a book) as long as it is clearly and explicitly indicated that the work was published for the first time in Revista Mexicana de Ciencias Agrícolas.
For all the above, the authors shall send the Letter-transfer of Property Rights for the first publication duly filled in and signed by the author(s). This form must be sent as a PDF file to: revista_atm@yahoo.com.mx; cienciasagricola@inifap.gob.mx; remexca2017@gmail.
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 International license.
