An Assessment of the Potential of Multispectral Sentinel-2 Satellite Imagery for Detecting Dubas Bug Infestations in Date Palm Cultivation Regions

Articles in Press

Document Type : Research Article-en

Authors

• H. Karimi ¹
• M. J. Assari ²
• F. Ranjbar-Varandi ³

¹ Agricultural Engineering Research Department, Kerman Agricultural and Resource Research and Education Center, AREEO, Kerman, Iran

² Plant Protection Research Department, Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Kerman, Iran

³ Department of Plant Protection, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

10.22067/jam.2025.90276.1297

Abstract

The Dubas bug (Ommatissus lybicus) poses a significant threat to agriculture in the Middle East by weakening palm trees and reducing fruit production. Effective pest control depends on accurate and timely localization of the infestation. However, regular field inspections are difficult and time-consuming, especially for large areas. This research investigates the potential of Sentinel-2 satellite imagery for detecting Dubas bug infestations. The aim is to improve monitoring capabilities, accelerate intervention strategies, and mitigate the associated economic impact. The field trial to assess the infestation occurred in May 2023, coinciding with the peak of the pest outbreak. The severity of the infestation was assessed through pest counts conducted in date palm groves within the urban area of Bam, Iran. Sentinel-2 multispectral images of a specific area were acquired and processed for correction, raw data preparation, and information extraction. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method was used for the atmospheric correction of the acquired images. The Nearest Neighbor Interpolation method was used to resample satellite images, standardizing all bands to a uniform 10-meter resolution. Following the pre-processing phase, the KD-tree-based K-Nearest Neighbor classifier model was selected to develop a model specifically designed for identifying areas infested by the Dubas bug. For training, 70% of the measured field data were used, including uninfested areas and areas with three levels of infestation from light to heavy, as well as other land features such as buildings, roads, etc. The remaining 30% of the data was utilized to evaluate the trained model, using the correct prediction rate as the assessment criterion. The trained classifier, validated against the ground truth data, achieved an accuracy of approximately 83% on the test dataset. This accuracy highlights the ability of Sentinel-2 multispectral imagery and machine learning to detect Dubas bug infestations in date palm groves and can facilitate targeted and sustainable pest management strategies.

Keywords

• Classification model
• Date fruit
• Field inspections
• Remote sensing

Main Subjects

• Precision Farming

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Adelabu, S., Mutanga, O., Adam, E., & Sebego, R. (2014). Spectral Discrimination of Insect Defoliation Levels in Mopane Woodland Using Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1), 177-186. https://doi.org/10.1109/JSTARS.2013.2258329
2. Adelabu, S., Mutanga, O., & Cho, M. A. (2012). A review of remote sensing of insect defoliation and its implications for the detection and mapping of Imbrasia belina defoliation of Mopane Woodland. The African Journal of Plant Science and Biotechnology, 6.
3. Al-Khatri, S. (2004). Date palm pests and their control. Paper presented at the Proceedings of date palm regional workshop on ecosystem-based IPM for date palm in gulf Countries.
4. Al-Khatri, S. A. H. (2011). Biological, ecological and phylogenic studies of Pseudoligosita babylonica viggiani, a native egg parasitoid of Dubas bug Ommatissus lybicus de Bergevin, the major pest of date palm in the Sultanate of Oman. University of Reading.
5. Al-Kindi, K. M., Kwan, P., Andrew, N., & Welch, M. (2017). Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman. PloS one, 12(5), e0178109. https://doi.org/10.1371/journal.pone.0178109
6. Al-Kindi, K. M., Kwan, P., Andrew, N., & Welch, M. (2019). Geospatial and Statistical Techniques for Modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) Habitat and Population Densities.
7. Al Sarai, A. (2015). Studies on the control of Dubas bug, Ommatissus lybicus DeBergevin (Homoptera: Tropiduchidae), a major pest of date palm in the Sultanate of Oman.
8. Al Shidi, R. H., Kumar, L., & Al-Khatri, S. A. (2019). Detecting Dubas bug infestations using high resolution multispectral satellite data in Oman. Computers and Electronics in Agriculture, 157, 1-11. https://doi.org/10.1016/j.compag.2018.12.037
9. Al Shidi, R. H., Kumar, L., Al-Khatri, S. A., Albahri, M. M., & Alaufi, M. S. (2018). Relationship of date palm tree density to Dubas bug Ommatissus lybicus infestation in Omani orchards. Agriculture, 8(5), 64. https://doi.org/10.3390/agriculture8050064
10. Baig, M. H. A., Lifu, Z., Tong, S., & Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5), 423-431. https://doi.org/10.1080/2150704X.2014.915434
11. Bernstein, L. S., Jin, X., Gregor, B., & Adler-Golden, S. M. (2012). Quick atmospheric correction code: algorithm description and recent upgrades. Optical engineering, 51(11), 111719. https://doi.org/10.1117/1.OE.51.11.111719
12. Carpenter, J. B., McMillen, J. M., Wengert, E. M., & Elmer, H. (1978). Pests and diseases of the date palm: US Department of Agriculture, Science and Education Administration.
13. EOS. (2018). Sentinel-2 imagery. Earth Observing System. Retrieved from https://eos.com/sentinel-2/
14. Haghighian, F., Yousefi, S., & Keesstra, S. (2022). Identifying tree health using sentinel-2 images: a case study on Tortrix viridana L. infected oak trees in Western Iran. Geocarto International, 37(1), 304-314. https://doi.org/10.1080/10106049.2020.1716397
15. Hicke, J. A., & Logan, J. (2009). Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery. International Journal of Remote Sensing, 30(17), 4427-4441. https://doi.org/10.1080/01431160802566439
16. Howard, F. (2001). Insect pests of palms and their control. Pesticide outlook, 12(6), 240-243. https://doi.org/10.1039/B110547G
17. Huang, W., Luo, J., Zhang, J., Zhao, C., & Wang, J. (2012). Crop Disease and Pest Monitoring by Remote Sensing. In B. Escalante (Ed.), Remote Sensing - Applications. Rijeka: IntechOpen.
18. Hussain, A. A. (1963). Biology and control of the dubas bug, Ommatissus binotatus lybicus de Berg. (Homoptera, Tropiduchidae), infesting date palms in Iraq. Bulletin of Entomological Research, 53(4), 737-745. https://doi.org/10.1017/S0007485300048458
19. IDB. (2024). Sentinel-2 RS indices. Retrieved from https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/indexdb/
20. Kauth, R. J., & Thomas, G. (1976). The tasselled cap--a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Paper presented at the LARS symposia.
21. Khan, A. L., Asaf, S., Khan, A., Khan, A., Imran, M., Al-Harrasi, A., ..., & Al-Rawahi, A. (2020). Transcriptomic analysis of Dubas bug (Ommatissus lybicus Bergevin) infestation to Date Palm. Scientific Reports, 10(1), 1-15. https://doi.org/10.1038/s41598-020-67438-z
22. Kulkarni, A., Chong, D., & Batarseh, F. A. (2020). 5 - Foundations of data imbalance and solutions for a data democracy. In F. A. Batarseh & R. Yang (Eds.), Data Democracy (pp. 83-106): Academic Press.
23. Kumbula, S. T., Mafongoya, P., Peerbhay, K. Y., Lottering, R. T., & Ismail, R. (2019). Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa. Remote Sensing, 11(3), 278. Retrieved from https://www.mdpi.com/2072-4292/11/3/278
24. Lottering, R., & Mutanga, O. (2016). Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 112, 13-22. https://doi.org/10.1016/j.isprsjprs.2015.11.010
25. Mahmoudi, M., Gheybi, M., & Khoshnoud, M. (2020). A study on different sampling techniques for dubas bug, Ommatissus lybicus (Hem.: Tropiduchidae). Plant Pest Research, 10(3). https://doi.org/10.22124/IPRJ.2020.4425
26. Mirik, M., Michels, G. J., Kassymzhanova-Mirik, S., & Elliott, N. C. (2007). Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture, 57(2), 123-134. https://doi.org/10.1016/j.compag.2007.03.002
27. Neteler, M., Roiz, D., Rocchini, D., Castellani, C., & Rizzoli, A. (2011). Terra and Aqua satellites track tiger mosquito invasion: modelling the potential distribution of Aedes albopictus in north-eastern Italy. International Journal of Health Geographics, 10(1), 1-14. https://doi.org/10.1186/1476-072X-10-49
28. Oumar, Z., & Mutanga, O. (2013). Using WorldView-2 bands and indices to predict bronze bug (Thaumastocoris peregrinus) damage in plantation forests. International Journal of Remote Sensing, 34(6), 2236-2249. https://doi.org/10.1080/01431161.2012.743694
29. Peerbhay, K., Ilaria, G., Romano, L., & Naicker, R. (2022). Remote sensing wattle rust induced defoliation across black wattle timber plantations in Southern Africa. International Journal of Remote Sensing, 43(6), 2212-2226. https://doi.org/10.1080/01431161.2022.2058891
30. Pontius, J., Schaberg, P., & Hanavan, R. (2020). Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance and Decline for Protection of Biodiversity. In J. Cavender-Bares, J. A. Gamon, & P. A. Townsend (Eds.), Remote Sensing of Plant Biodiversity (pp. 121-154). Cham: Springer International Publishing.
31. Reisig, D., & Godfrey, L. (2006). Remote Sensing for Detection of Cotton Aphid– (Homoptera: Aphididae) and Spider Mite– (Acari: Tetranychidae) Infested Cotton in the San Joaquin Valley. Environmental Entomology, 35(6), 1635-1646. https://doi.org/10.1093/ee/35.6.1635
32. Rostami, M. A., Assari, M. J., Pejman, A., & Shaker, M. (2019). Preparation of distribution and severity map for Dubas bug (Ommatissus lybicus) in Bam region by Geographic Information System. Plant Pests Research, 9(1), 63-73. https://doi.org/10.22124/iprj.1970.3432
33. Rouse, J., Haas, J., Schell, J., & Deering, D. (1974). Monitoring vegetation systemsin the Great Plains with erts. Paper presented at the Proceedings of the 3rd ERTS Symposium, Washington, DC, USA.
34. Saadikhani, M., Maharlooei, M., Rostami, M. A., & Edalat, M. (2023). Fusion of Multispectral and Radar Images to Enhance Classification Accuracy and Estimate the Area under Various Crops Cultivation. Journal of Agricultural Machinery, 13(4), 493-508. https://doi.org/10.22067/jam.2022.78446.1123
35. Segarra, J., Buchaillot, M. L., Araus, J. L., & Kefauver, S. C. (2020). Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy, 10(5). https://doi.org/10.3390/agronomy10050641
36. Shah, A., Naeem, M., Nasir, M. F., Irfan-ul-Haq, M., & Hafeez, Z. (2012). Biology of Dubas Bug, Ommatissus lybicus (Homoptera: Tropiduchidae), a Pest on Date Palm During Spring and Summer Seasons in Panjgur, Pakistan. Pakistan Journal of Zoology, 44(6).
37. Shah, A., Zia, A., Rafi, M. A., Mehmood, S. A., Aslam, S., & Chaudhry, M. T. (2016). Quantification of honeydew production caused by dubas bug on three date palm cultivars. Journal of Entomology and Zoology Studies, 4, 478-484.
38. Silva, C. R., Olthoff, A., de la Mata, J. A. D., & Alonso, A. P. (2013). Remote monitoring of forest insect defoliation. A review. Forest Systems, 22(3), 377-391. https://doi.org/10.5424/fs/2013223-04417
39. Tiwari, A. (2022). Chapter 2- Supervised learning: From theory to applications. In R. Pandey, S. K. Khatri, N. k. Singh, & P. Verma (Eds.), Artificial Intelligence and Machine Learning for EDGE Computing (pp. 23-32): Academic Press.
40. Wakil, W., Faleiro, J. R., & Miller, T. A. (2015). Sustainable pest management in date palm: current status and emerging challenges: Springer.
41. Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402
42. Yuan, L., Pu, R., Zhang, J., Wang, J., & Yang, H. (2016). Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale. Precision Agriculture, 17(3), 332-348. https://doi.org/10.1007/s11119-015-9421-x
43. Zhang, J., Pu, R., Yuan, L., Wang, J., Huang, W., & Yang, G. (2014). Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery. PloS one, 9(4), e93107. https://doi.org/10.1371/journal.pone.0093107