1. Home
  2. Vol 11, No 3 (2025)
  3. Cahyadi

Integration of Low-Cost GNSS and Multispectral Camera to Increase Oil Palm Position Accuracy and Health Monitoring

M. N. Cahyadi, M. A. Syariz, F. Taufany, . Lisnawita, S. S. Wismaroh, D. Kusumawardani, T. B. Saputro, F. Haq, M. C. Laksmana, L. A. Triawan

Abstract


The Global Navigation Satellite System facilitates efficient agricultural initiatives, resolving land ownership and precise plantation monitoring issues. The oil palm sector is deeply integrated into various economies due to the world's use in food supplies, cosmetics, and oil biodiesel production. However, local farmers have trouble managing the plantation’s condition and land ownership due to the underdeveloped modern technology at their disposal. The Normalized Difference Vegetation Index was employed in order to assess the NDVI camera oil palm tree growth, utilizing a MAPIR Survey3 RGN Multispectral Camera integrated with red, green, and near IR sensors. Images were taken directly on the surface level to enable focused analysis on the palm trees. This included the use of an MPAR calibration ground target placed beside the leaves for data accuracy and an operator that held the camera to the trees. Utilizing this strategy allowed for a more intricate and detailed analysis of each oil palm tree, and due to the coordination of the trees, aerial images were produced to create a detailed image. Low-cost GNSS instruments alongside RTK technology were employed in determining the coordinate position of the oil palm trees. Considerable relationships were found between NDVI and content in chlorophyll: NDVI-G and Chl a (r = 0.679), NDVI-B and Chl a (r = 0.618), and NDVI-B and Chl b(r = 0.657). The positional errors obtained varied within –0.105 to 0.166 meters for low-cost GNSS and –0.159 to 0.083 meters for geodetic GNSS, the latter recording the least MAE of 0.053. This research work found a cheap and accurate oil palm growth monitoring system using multispectral sensors. This method overcomes the technological gap of local farmers and provides an alternative strategy for the management of plantations.

 

Doi: 10.28991/CEJ-2025-011-03-010

Full Text: PDF


Keywords


Oil Palm; NDVI; Multispectral Camera; GNSS; Monitoring.

References


Dash, J. P., Pearse, G. D., & Watt, M. S. (2018). UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sensing, 10(8), 1216. doi:10.3390/rs10081216.

Watt, P., Meredith, A., Yang, C., & Watt, M. S. (2013). Development of regional models of Pinus radiata height from GIS spatial data supported with supplementary satellite imagery. New Zealand Journal of Forestry Science, 43(4), 1–11. doi:10.1186/1179-5395-43-11.

Marx, A., McFarlane, D., & Alzahrani, A. (2017). UAV data for multi-temporal Landsat analysis of historic reforestation: a case study in Costa Rica. International Journal of Remote Sensing, 38(8–10), 2331–2348. doi:10.1080/01431161.2017.1280637.

Ecke, S., Stehr, F., Frey, J., Tiede, D., Dempewolf, J., Klemmt, H. J., Endres, E., & Seifert, T. (2024). Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning. Computers and Electronics in Agriculture, 219, 108785. doi:10.1016/j.compag.2024.108785.

FAA. (2024). 2024 FAA-EASA International Aviation Safety Conference. Federal Aviation Administration (FAA), Washington, United States. Available online: https://www.faa.gov/aircraft/air_cert/international/us_eu_conference (accessed on February 2024).

Shaharum, N. S. N., Shafri, H. Z. M., Ghani, W. A. W. A. K., Samsatli, S., Al-Habshi, M. M. A., & Yusuf, B. (2020). Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms. Remote Sensing Applications: Society and Environment, 17, 100287. doi:10.1016/j.rsase.2020.100287.

Septiarini, A., Hamdani, H., Hardianti, T., Winarno, E., Suyanto, S., & Irwansyah, E. (2021). Pixel quantification and color feature extraction on leaf images for oil palm disease identification. 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 1–5. doi:10.1109/iceeie52663.2021.9616645.

Mazeh, F., El Sahili, J., & Zaraket, H. (2021). Low-Cost NDVI platform for land operation: Passive and active. IEEE Sensors Letters, 5(10), 1–4. doi:10.1109/LSENS.2021.3112822.

Stamford, J. D., Vialet-Chabrand, S., Cameron, I., & Lawson, T. (2023). Development of an accurate low cost NDVI imaging system for assessing plant health. Plant Methods, 19(1), 9. doi:10.1186/s13007-023-00981-8.

Marzukhi, F., Elahami, A. L., & Bohari, S. N. (2016). Detecting nutrients deficiencies of oil palm trees using remotely sensed data. IOP Conference Series: Earth and Environmental Science, 37(1), 12040. doi:10.1088/1755-1315/37/1/012040.

Tugi, A., Rasib, A. W., Suri, M. A., Zainon, O., Mohd Yusoff, A. R., Abdul Rahman, M. Z., Sari, N. A., & Darwin, N. (2015). Oil palm tree growth monitoring for smallholders by using unmanned aerial vehicle. Jurnal Teknologi, 77(26), 17–27. doi:10.11113/jt.v77.6855.

Wang, Q., Tenhunen, J., Dinh, N. Q., Reichstein, M., Vesala, T., & Keronen, P. (2004). Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sensing of Environment, 93(1–2), 225–237. doi:10.1016/j.rse.2004.07.006.

Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15–30. doi:10.1016/j.aac.2022.10.001.

Purwanto, E., Santoso, H., Jelsma, I., Widayati, A., Nugroho, H. Y. S. H., & van Noordwijk, M. (2020). Agroforestry as policy option for forest-zone oil palm production in indonesia. Land, 9(12), 1–34. doi:10.3390/land9120531.

Dhaouadi, L., Besser, H., Karbout, N., Al-Omran, A., Wassar, F., Wahba, M. S., Yaohu, K., & Hamed, Y. (2021). Irrigation water management for sustainable cultivation of date palm. Applied Water Science, 11(11). doi:10.1007/s13201-021-01507-0.

MAPIR Camera. (2025). Affordable remote sensing solutions for any environment. MAPIR Camera, San Diego, United States. Available online: https://www.mapir.camera/en-gb (accessed on February 2025).

Alkan, R. M., Erol, S., İlçi, V., & Ozulu, M. (2020). Comparative analysis of real-time kinematic and PPP techniques in dynamic environment. Measurement: Journal of the International Measurement Confederation, 163, 107995. doi:10.1016/j.measurement.2020.107995.

Majid, N. A., Ramli, Z., Sum, S. M., & Awang, A. H. (2021). Sustainable palm oil certification scheme frameworks and impacts: A systematic literature review. Sustainability (Switzerland), 13(6), 3263. doi:10.3390/su13063263.

Absalome, M. A., Massara, C. C., Alexandre, A. A., Gervais, K., Chantal, G. G. A., Ferdinand, D., Rhedoor, A. J., Coulibaly, I., George, T. G., Brigitte, T., Marion, M., & Jean-Paul, C. (2020). Biochemical properties, nutritional values, health benefits and sustainability of palm oil. Biochimie, 178, 81–95. doi:10.1016/j.biochi.2020.09.019.

Siswantoro, J., Artadana, I. B. M., & Siswantoro, M. Z. F. N. (2022). Leaf geometric properties measurement using computer vision system based on camera parameters. International Conference on Informatics, Technology, and Engineering 2021 (InCITE 2021): Leveraging Smart Engineering, 2470, 050008. doi:10.1063/5.0080190.

Gantimurova, S., Parshin, A., & Erofeev, V. (2021). GIS-based landslide susceptibility mapping of the circum-baikal railway in Russia using UAV data. Remote Sensing, 13(18), 3629. doi:10.3390/rs13183629.

Sabri, S. A., Mohd., Endut, R., B. M. Rashidi, C., R. Laili, A., A. Aljunid, S., & Ali, N. (2019). Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves. Bulletin of Electrical Engineering and Informatics, 8(2), 506–513. doi:10.11591/eei.v8i2.1412.

Saini, A., Singh, J., & Kant, R. (2022). Comparative Study of Estimation Methods for Efficient Extraction of Chlorophyll a and Carotenoids Using Different Solvents. Bulletin of Pure & Applied Sciences- Botany, 41b(2), 79–86. doi:10.5958/2320-3196.2022.00009.x.

Ali, K. A., Noraldeen, S. S., & Yaseen, A. A. (2021). An Evaluation Study for Chlorophyll Estimation Techniques. Sarhad Journal of Agriculture, 37(4), 1458–1465. doi:10.17582/journal.sja/2021/37.4.1458.1465.

Khorramnia, K., Khot, L. R., Shariff, A. R. B. M., Ehsani, R., Mansor, S. B., & Rahim, A. B. A. (2014). Oil palm leaf nutrient estimation by optical sensing techniques. Transactions of the ASABE, 57(4), 1267-1277. doi:10.13031/trans.57.10142.

Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., Penuelas, J., & Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1), 28–41. doi:10.2307/1942049.

Silva, S. A. S., Ferraz, G. A. S., Figueiredo, V. C., Volpato, M. M. L., Machado, M. L., Silva, V. A., ... & Bambi, G. (2024). Spatial variability of chlorophyll and NDVI obtained by different sensors in an experimental coffee field. Agronomy Research, 22(X), 1-16. doi:10.15159/AR.24.037.

Šverko, Z., Vrankić, M., Vlahinić, S., & Rogelj, P. (2022). Complex Pearson Correlation Coefficient for EEG Connectivity Analysis. Sensors, 22(4), 1477. doi:10.3390/s22041477.

Rodríguez-López, L., Duran-Llacer, I., González-Rodríguez, L., Abarca-del-Rio, R., Cárdenas, R., Parra, O., Martínez-Retureta, R., & Urrutia, R. (2020). Spectral analysis using LANDSAT images to monitor the chlorophyll-a concentration in Lake Laja in Chile. Ecological Informatics, 60, 101183. doi:10.1016/j.ecoinf.2020.101183.

Janos, D., & Kuras, P. (2021). Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode. Sensors, 21(16), 5552. doi:10.3390/s21165552.

Rosner, B. (1982). A Generalization of the Paired t-Test. Applied Statistics, 31(1), 9. doi:10.2307/2347069.

Elaksher, A., Ali, T., Kamtchang, F., Wegmann, C., & Guerrero, A. (2020). Performance analysis of multi-GNSS static and RTK techniques in estimating height differences. International Journal of Digital Earth, 13(5), 586-601. doi:10.1080/17538947.2018.1550118.

Abid, M. A., Husein, H. N., & Hamed, N. H. (2020). Analysis of Error in Geodetic Networks using Different Observation Methods. IOP Conference Series: Materials Science and Engineering 737(1), 012234. doi:10.1088/1757-899X/737/1/012234.

Štern, A., & Kos, A. (2018). Positioning performance assessment of geodetic, automotive, and smartphone GNSS receivers in standardized road scenarios. IEEE access, 6, 41410-41428. doi:10.1109/ACCESS.2018.2856521.

Paziewski, J., Sieradzki, R., & Baryla, R. (2018). Multi-GNSS high-rate RTK, PPP and novel direct phase observation processing method: Application to precise dynamic displacement detection. Measurement Science and technology, 29(3), 035002. doi:10.1088/1361-6501/aa9ec2.

Caruso, G., Tozzini, L., Rallo, G., Primicerio, J., Moriondo, M., Palai, G., & Gucci, R. (2017). Estimating biophysical and geometrical parameters of grapevine canopies ('Sangiovese’) by an unmanned aerial vehicle (UAV) and VIS-NIR cameras. VITIS-Journal of Grapevine Research, 56(2), 63–70. doi:10.5073/vitis.2017.56.63-70.

Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., ... & Chen, M. (2022). Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment, 3(7), 477-493. doi:10.1038/s43017-022-00298-5.

Kizilgeci, F., Yildirim, M., Islam, M. S., Ratnasekera, D., Iqbal, M. A., & Sabagh, A. E. L. (2021). Normalized difference vegetation index and chlorophyll content for precision nitrogen management in durum wheat cultivars under semi-arid conditions. Sustainability (Switzerland), 13(7), 3725. doi:10.3390/su13073725.

Coyne, P. I., Aiken, R. M., Maas, S. J., & Lamm, F. R. (2009). Evaluating yield tracker forecasts for maize in Western Kansas. Agronomy Journal, 101(3), 671–680. doi:10.2134/agronj2008.0146.

Jopia, A., Zambrano, F., Pérez-Martínez, W., Vidal-Páez, P., Molina, J., & Mardones, F. de la H. (2020). Time-series of vegetation indices (VNIR/SWIR) derived from sentinel-2 (A/B) to assess turgor pressure in Kiwifruit. ISPRS International Journal of Geo-Information, 9(11), 641. doi:10.3390/ijgi9110641.

Taufik, M., Yuwono, Cahyadi, M. N., & Putra, J. R. (2019). Analysis level of accuracy GNSS observation processing using u-blox as low-cost GPS and geodetic GPS (case study: M8T). IOP Conference Series: Earth and Environmental Science, 389, 012041. doi:10.1088/1755-1315/389/1/012041.


Full Text: PDF

DOI: 10.28991/CEJ-2025-011-03-010

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2025 Mokhamad Nur Cahyadi, Muhammad Aldila Syariz, Fadlilatul Taufany, Lisnawita Lisnawita, Wismaroh Sanniwati Saragih, Deni Kusumawardani, Triono Bagus Saputro, Failaqul Haq, Miko Cahya Laksmana, Luki Adi Triawan

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message