Tree Detection and NDVI-Based Plant Health Assessment Using Object-Based Image Analysis on Multispectral UAV Imagery

Year 2025, Volume: 27 Issue: 2, 183 - 209

Abdurahman Yasin Yiğit , Osman Orhan

https://doi.org/10.24011/barofd.1639471

Abstract

Efficient management of agricultural lands and conservation of natural resources are critical objectives in modern environmental science. Remote sensing technologies, particularly Unmanned Aerial Vehicle (UAV)-based imaging systems, provide powerful tools to achieve these goals. UAV imagery has become increasingly valuable in plant health assessment and tree detection due to its high-resolution data and ability to cover extensive areas, overcoming many limitations of traditional methods. Spectral analysis techniques, such as the Normalized Difference Vegetation Index (NDVI), offer effective means for evaluating vegetation health and identifying stressed regions. In this study, we employed UAV imagery combined with Object-Based Image Analysis (OBIA) to detect trees and assess plant health in agricultural fields. NDVI values derived from multispectral images were integrated with segmentation and classification workflows to accurately identify individual trees. Accuracy assessments demonstrated robust model performance, with classification accuracy reaching 89% and precision at 92%. Furthermore, NDVI analyses enabled the differentiation of healthy, moderately healthy, and stressed vegetation, allowing detailed spatial mapping of plant health across the study area. Overall, our results indicate that UAV-based imaging and OBIA represent a powerful approach for agricultural management and environmental monitoring. The demonstrated accuracy and operational ease of these methods provide a solid foundation for future applications across diverse geographic regions and larger scales.

Keywords

Agricultural management, multispectral imaging, object oriented image analysis, tree detection, plant health

Thanks

We would like to thank Map Engineer Mert Anıl Ateş for his valuable contributions to the office operations and various support processes of this study.

Multispektral İHA Görüntüleri Kullanılarak Nesne Tabanlı Görüntü Analizi ile Ağaç Tespiti ve NDVI Tabanlı Bitki Sağlığı Analizi

Abstract

Tarımsal alanların verimli bir şekilde yönetimi ve doğal kaynakların korunması, modern çevre biliminin en önemli hedeflerinden biridir. Bu hedeflere ulaşmak için uzaktan algılama teknolojileri, özellikle İnsansız Hava Aracı (İHA) tabanlı görüntüleme sistemleri, güçlü araçlar sunmaktadır. Geleneksel yöntemlerin sınırlılıklarını aşarak, İHA görüntüleri yüksek çözünürlükte veri sağlaması ve geniş alanları kapsayabilmesi nedeniyle bitki sağlığı ve ağaç tespiti çalışmalarında önemli bir yer edinmiştir. Özellikle Normalize Edilmiş Fark Bitki Örtüsü İndeksi (Normalized Difference Vegetation Index/NDVI) gibi spektral analiz yöntemleri, bitki örtüsünün sağlık durumunu değerlendirmek ve stres altındaki bölgeleri belirlemek için etkili bir çözüm sunmaktadır. Bu çalışmada, İHA görüntüleri ve nesne tabanlı görüntü analizi yöntemleri kullanılarak tarımsal alanlardaki ağaçların tespiti ve bitki sağlığının değerlendirilmesi amaçlanmıştır. Multispektral görüntülerden elde edilen NDVI değerleri, segmentasyon ve sınıflandırma süreçleri ile birleştirilerek ağaçların etkili bir şekilde tespit edilmesi sağlanmıştır. Doğruluk analizleri, modelin genel performansını ortaya koymuş ve segmentasyon süreçlerinin başarı oranını doğrulamıştır. Model, %89 doğruluk ve %92 kesinlik oranıyla etkili bir sınıflandırma gerçekleştirmiştir. Ayrıca, NDVI analizleri ile sağlıklı, orta sağlıkta ve stres altındaki bitkiler ayrıştırılmış, çalışma alanındaki mekânsal farklılıklar detaylı bir şekilde haritalandırılmıştır. Sonuç olarak, bu çalışma, İHA tabanlı görüntüleme ve Nesne Tabanlı Görüntü Analizi (Object-Based Image Analysis/OBIA) yöntemlerinin tarımsal yönetim ve çevresel izleme uygulamaları için güçlü bir çözüm sunduğunu göstermiştir. Bu yöntemlerin doğruluğu ve uygulama kolaylığı, gelecekte farklı coğrafyalarda ve daha geniş alanlarda yapılacak çalışmalara ışık tutmaktadır.

Keywords

Tarımsal yönetim, multispektral görüntüleme, nesne tabanlı görüntü analizi, ağaç tespiti, bitki sağlığı

Thanks

Bu çalışmanın ofis işlemlerinde ve çeşitli destek süreçlerinde gösterdiği değerli katkılarından dolayı Harita Mühendisi Mert Anıl Ateş’e teşekkür ederiz.

References

• Adhikari, A., Kumar, M., Agrawal, S., & Raghavendra, S. (2021). An integrated object and machine learning approach for tree canopy extraction from UAV datasets. Journal of the Indian Society of Remote Sensing, 49(3), 471–478. https://doi.org/10.1007/s12524-020-01240-2.
• Aguilar, M. A., Jiménez-Lao, R., & Aguilar, F. J. (2021). Evaluation of object-based greenhouse mapping using WorldView-3 VNIR and SWIR data: A case study from Almeria (Spain). Remote Sensing, 13(11), 2133.
• Akca, S., & Polat, N. (2022). Semantic segmentation and quantification of trees in an orchard using UAV orthophoto. Earth Science Informatics, 15(4), 2265–2274.
• Baatz, M., & Schäpe, A. (1999). Object-oriented and multiscale image analysis in semantic networks. In Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing (pp. xx–xx). Enschede: ITC.
• Bergsjö, J. (2014). Object-based change detection in urban areas using KTH-SEG. Bachelor Thesis, KTH Royal Institute of Technology, Stockholm.
• Berra, E. F., & Peppa, M. V. (2020, March). Advances and challenges of UAV SfM MVS photogrammetry and remote sensing: Short review. In 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) (pp. 533–538). IEEE.
• Bilgilioğlu, B. B. (2015). Uzaktan algılanmış görüntülerden faydalanılarak nesne tabanlı sınıflandırma yöntemi ile kent merkezlerindeki detayların çıkarımı ve yorumlanması. Yüksek Lisans Tezi, Aksaray Üniversitesi, Fen Bilimleri Enstitüsü, Aksaray.
• Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004.
• Blaschke, T. (2010). Object-based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
• Blaschke, T., Kelly, M., & Merschdorf, H. (2024). Object-based image analysis. In Remote Sensing Handbook, Volume II: Image Processing, Change Detection, GIS, and Spatial Data Analysis (pp. 145–xx). Springer.
• Bryson, M., Johnson-Roberson, M., Murphy, R., & Bongiorno, J. (2013). Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes. PLoS ONE, 8(9), e73550. https://doi.org/10.1371/journal.pone.0073550.
• Buja, I., Sabella, E., Monteduro, A. G., Chiriacò, M. S., De Bellis, L., Luvisi, A., & Maruccio, G. (2021). Advances in plant disease detection and monitoring: From traditional assays to in-field diagnostics. Sensors, 21(6), 2129.
• Chehreh, B., Moutinho, A., & Viegas, C. (2023). Latest trends on tree classification and segmentation using UAV data—A review of agroforestry applications. Remote Sensing, 15(9), 2263.
• Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Bui, D. T., ... & Zhu, A. X. (2018). GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical-based random forest method. Catena, 164, 135–149.
• Chen, W., Xie, X., Peng, J., Wang, J., Duan, Z., & Hong, H. (2017). GIS-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomatics, Natural Hazards and Risk, 8(2), 950–973.
• D’Andrea, G., Šimůnek, V., Castellaneta, M., Vacek, Z., Vacek, S., Pericolo, O., ... & Ripullone, F. (2022). Mismatch between annual tree-ring width growth and NDVI index in Norway spruce stands of Central Europe. Forests, 13(9), 1417.
• Dai, W., Qiu, R., Wang, B., Lu, W., Zheng, G., Amankwah, S. O. Y., & Wang, G. (2023). Enhancing UAV-SfM photogrammetry for terrain modeling from the perspective of spatial structure of errors. Remote Sensing, 15(17), 4305.
• de Castro, A. I., Torres-Sánchez, J., Peña, J. M., Jiménez-Brenes, F. M., Csillik, O., & López-Granados, F. (2018). An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sensing, 10(2), 285. https://doi.org/10.3390/rs10020285.
• Deliry, S. I., & Avdan, U. (2021). Accuracy of unmanned aerial systems photogrammetry and structure from motion in surveying and mapping: A review. Journal of the Indian Society of Remote Sensing, 49(8), 1997–2017.
• Dwikarsa, Y., & Basith, A. (2021). Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters. Communications in Science and Technology, 6(1), 55–59.
• Ecke, S., Dempewolf, J., Frey, J., Schwaller, A., Endres, E., Klemmt, H. J., ... & Seifert, T. (2022). UAV-based forest health monitoring: A systematic review. Remote Sensing, 14(13), 3205.
• Fernández-Hernandez, J., González-Aguilera, D., Rodríguez-Gonzálvez, P., & Mancera-Taboada, J. (2015). Image-based modelling from unmanned aerial vehicle (UAV) photogrammetry: An effective, low-cost tool for archaeological applications. Archaeometry, 57, 128–145. https://doi.org/10.1111/arcm.12078.
• Ferro, M. V., Sørensen, C. G., & Catania, P. (2024). Comparison of different computer vision methods for vineyard canopy detection using UAV multispectral images. Computers and Electronics in Agriculture, 225, 109277.
• Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: A new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38, 421–430. https://doi.org/10.1002/esp.3366.
• Ghasemi, M., Latifi, H., & Pourhashemi, M. (2022). A novel method for detecting and delineating coppice trees in UAV images to monitor tree decline. Remote Sensing, 14(23), 5910. https://doi.org/10.3390/rs14235910.
• Goldbergs, G., Maier, S. W., Levick, S. R., & Edwards, A. (2018). Efficiency of individual tree detection approaches based on lightweight and low-cost UAS imagery in Australian Savannas. Remote Sensing, 10(2), 161.
• Gomez, C., Hayakawa, Y., & Obanawa, H. (2015). A study of Japanese landscapes using structure-from-motion derived DSMs and DEMs based on historical aerial photographs: New opportunities for vegetation monitoring and diachronic geomorphology. Geomorphology, 242, 11–20. https://doi.org/10.1016/j.geomorph.2015.02.021.
• Govi, D., Pappalardo, S. E., De Marchi, M., & Meggio, F. (2024). From space to field: Combining satellite, UAV and agronomic data in an open-source methodology for the validation of NDVI maps in precision viticulture. Remote Sensing, 16(5), 735.
• Grippa, T., Lennert, M., Beaumont, B., Vanhuysse, S., Stephenne, N., & Bontemps, S. (2017). An open-source semi-automated processing chain for urban object-based classification. Remote Sensing, 9(4), 358. https://doi.org/10.3390/rs9040358.
• Guo, Q., Zhang, J., Guo, S., Ye, Z., Deng, H., Hou, X., & Zhang, H. (2022). Urban tree classification based on object-oriented approach and random forest algorithm using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing, 14(16), 3885. https://doi.org/10.3390/rs14163885.
• Han, P., Ma, C., Chen, J., Chen, L., Bu, S., Xu, S., ... & Hagino, T. (2022). Fast tree detection and counting on UAVs for sequential aerial images with generating orthophoto mosaicing. Remote Sensing, 14(16), 4113.
• Haque, M. A., Reza, M. N., Ali, M., Karim, M. R., Ahmed, S., Lee, K. D., ... & Chung, S. O. (2024). Effects of environmental conditions on vegetation indices from multispectral images: A review. Korean Journal of Remote Sensing, 40(4), 319–341.
• Hemmateenejad, F., Fallati, L., Panieri, G., Ribeiro, P. A., Fusca, C., Ferré, B., & Savini, A. (2024). The role of substrate attributes as a driver for benthic epifaunal communities investigated applying OBIA techniques and image analysis on the Norskebanken cold seep site (Arctic Ocean). Copernicus Meetings.
• Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development Discussions, 2022, 1–10.
• Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from motion photogrammetry in forestry: A review. Current Forestry Reports, 5, 155–168.
• Ikendi, S. (2023). Ecological conservation, biodiversity, and agricultural education as integrated approaches for envisioning the future of sustainable agriculture in North America. International Journal of Sustainable Development & World Ecology, 30(2), 152–163.
• Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote sensing, 4(9), 2661-2693.
• Jalbuena, R. L., Peralta, R. V., & Tamondong, A. M. (2016, October). Mangrove classification through the use of object-oriented classification and support vector machine of LiDAR datasets: A case study in Naawan and Manticao, Misamis Oriental, Philippines. In Earth Resources and Environmental Remote Sensing/GIS Applications VII (Vol. 10005, pp. 367–375). SPIE.
• Jang, G., Kim, J., Yu, J. K., Kim, H. J., Kim, Y., Kim, D. W., ... & Chung, Y. S. (2020). Cost-effective unmanned aerial vehicle (UAV) platform for field plant breeding application. Remote Sensing, 12(6), 998.
• Javernick, L., Brasington, J., & Caruso, B. (2014). Modeling the topography of shallow braided rivers using structure-from-motion photogrammetry. Geomorphology, 213, 166–182. https://doi.org/10.1016/j.geomorph.2014.01.006.
• Jiang, N., Zhang, J. X., Li, H. T., & Lin, X. G. (2008). Object-oriented building extraction by DSM and very high-resolution orthoimages. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 441–446.
• Jiang, S., Jiang, C., & Jiang, W. (2020). Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 230–251.
• Kampouri, M., Kolokoussis, P., Argialas, D., & Karathanassi, V. (2019). Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece. Geocarto International, 34(12), 1273–1285.
• Kraus, K. (2011). Photogrammetry: Geometry from images and laser scans. Walter de Gruyter. Lang, S., Kienberger, S., & Hagenlocher, M. (2024). Object-based regionalization. In Remote Sensing Handbook, Volume II: Image Processing, Change Detection, GIS, and Spatial Data Analysis (pp. 385–xx). Springer.
• Li, X., Xiong, B., Yuan, Z., He, K., Liu, X., Liu, Z., & Shen, Z. (2021). Evaluating the potentiality of using control-free images from a mini Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) photogrammetry to measure paleoseismic offsets. International Journal of Remote Sensing, 42(7), 2417–2439.
• Madhuanand, L., Philippart, C. J., Nijland, W., de Jong, S. M., Bijleveld, A. I., & Addink, E. A. (2024). Optimizing predictions of environmental variables and species distributions on tidal flats by combining Sentinel-2 images and their deep-learning features with OBIA. International Journal of Remote Sensing, 1–24.
• Mahdy, M. (2024). Developing a transferable object-based image analysis (OBIA) approach for the detection and classification of urban features. Master's thesis, Universidade NOVA de Lisboa.
• Marino, L., Fallati, L., Varzi, A., Fersini, G., Piazzolla, D., Scanu, S., ... & Savini, A. (2024). Object-based image analysis on acoustic remote sensing data to support restoration actions on a Posidonia oceanica meadow offshore Civitavecchia (Eastern Tyrrhenian margin, Mediterranean Sea). Applied Sciences, 14(8), 3264.
• McLennon, E., Dari, B., Jha, G., Sihi, D., & Kankarla, V. (2021). Regenerative agriculture and integrative permaculture for sustainable and technology-driven global food production and security. Agronomy Journal, 113(6), 4541–4559.
• Mensah, J. (2019). Sustainable development: Meaning, history, principles, pillars, and implications for human action: Literature review. Cogent Social Sciences, 5(1), 1653531.
• Nhemachena, C., Nhamo, L., Matchaya, G., Nhemachena, C. R., Muchara, B., Karuaihe, S. T., & Mpandeli, S. (2020). Climate change impacts on water and agriculture sectors in Southern Africa: Threats and opportunities for sustainable development. Water, 12(10), 2673.
• Noori, A. M., Al-Saedi, A. S. J., & Abed, F. M. (2024). Optimizing application of UAV-based SfM photogrammetric 3D mapping in urban areas. Iraqi Journal of Science, 65(5).
• Orhan, O., Bilgilioglu, S. S., Kaya, Z., Ozcan, A. K., & Bilgilioglu, H. (2022). Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37(10), 2795–2820.
• Parrot Bluegrass. (2018). Parrot Bluegrass specification. The Drone Pro Shop. https://www.thedroneproshop.com/blogs/product-info/parrot-bluegrass-specification [ Erişim tarihi: 29.12.2024].
• Rizzo, D. M., Lichtveld, M., Mazet, J. A., Togami, E., & Miller, S. A. (2021). Plant health and its effects on food safety and security in a One Health framework: Four case studies. One Health Outlook, 3(1), 6.
• Ruess, S., Paulus, G., & Lang, S. (2024). Automated derivation of vine objects and ecosystem structures using UAS-based data acquisition, 3D point cloud analysis, and OBIA. Applied Sciences, 14(8), 3264.
• Sari, A. K., Priyono, K. D., & Rosyadi, Y. (2024, June). Estimation of oil palm tree quantity using oil palm Ecognition and SAGA GIS based on UAV imagery (Case Study: Dumai, Riau). In IOP Conference Series: Earth and Environmental Science (Vol. 1357, No. 1, p. 012028). IOP Publishing.
• Sharma, S., Beslity, J. O., Rustad, L., Shelby, L. J., Manos, P. T., Khanal, P., ... & Khanal, C. (2024). Remote sensing and GIS in natural resource management: Comparing tools and emphasizing the importance of in-situ data. Remote Sensing, 16(22), 4161.
• Snavely, N., Seitz, S. M., & Szeliski, R. (2008). Modeling the world from internet photo collections. International Journal of Computer Vision, 80(2), 189–210. https://doi.org/10.1007/s11263-007-0107-3.
• Sun, H., Yan, H., Hassanalian, M., Zhang, J., & Abdelkefi, A. (2023). UAV platforms for data acquisition and intervention practices in forestry: Towards more intelligent applications. Aerospace, 10(3), 317.
• Tarmizi, N. M., & Rizwan, N. I. (2024). Integrated supervised classification of LULC in identifying Musang King Durian illegal farming location. Jurnal Sylva Lestari, 12(2), 459–479.
• Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S. F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., & Wallace, L. (2017). Forestry applications of UAVs in Europe: A review. International Journal of Remote Sensing, 38(10), 2427–2447.
• Torres-Sánchez, J., de Castro, A. I., Peña, J. M., Jiménez-Brenes, F. M., Arquero, O., Lovera, M., & López-Granados, F. (2018). Mapping the 3D structure of almond trees using UAV-acquired photogrammetric point clouds and object-based image analysis. Biosystems Engineering, 176, 172–184. https://doi.org/10.1016/j.biosystemseng.2018.10.018.
• Vasuki, Y., Holden, E. J., Kovesi, P., & Micklethwaite, S. (2014). Semi-automatic mapping of geological structures using UAV-based photogrammetric data: An image analysis approach. Computers & Geosciences, 69, 22–32. https://doi.org/10.1016/j.cageo.2014.04.012.
• Velazquez-Chavez, L. J., Daccache, A., Mohamed, A. Z., & Centritto, M. (2024). Plant-based and remote sensing for water status monitoring of orchard crops: Systematic review and meta-analysis. Agricultural Water Management, 303, 109051.
• Vélez, S., Martínez-Peña, R., & Castrillo, D. (2023). Beyond vegetation: A review unveiling additional insights into agriculture and forestry through the application of vegetation indices. J, 6(3), 421–436.
• Wallace, L., Lucieer, A., Malenovský, Z., Turner, D., & Vopěnka, P. (2016). Assessment of forest structure using two UAV techniques: A comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 7(6), 62.
• Wang, X., Zhang, J., Xun, L., Wang, J., Wu, Z., Henchiri, M., ... & Yu, X. (2022). Evaluating the effectiveness of machine learning and deep learning models combined time-series satellite data for multiple crop types classification over a large-scale region. Remote Sensing, 14(10), 2341.
• Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). Structure-from-motion photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021.
• Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884–893. https://doi.org/10.1016/j.jag.2011.06.008.
• Wicaksono, A., & Hernina, R. (2021). Urban tree analysis using unmanned aerial vehicle (UAV) images and object-based classification (Case study: University of Indonesia campus). IOP Conference Series: Earth and Environmental Science, 683(1), 012105. https://doi.org/10.1088/1755-1315/683/1/012105.
• Wolf, B. M., & Heipke, C. (2007). Automatic extraction and delineation of single trees from remote sensing data. Machine Vision and Applications, 18(5), 317–330.
• Xu, Z., Shen, X., Cao, L., Coops, N. C., Goodbody, T. R., Zhong, T., ... & Wu, X. (2020). Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests. International Journal of Applied Earth Observation and Geoinformation, 92, 102173.
• Yang, K., Zhang, H., Wang, F., & Lai, R. (2022). Extraction of broad-leaved tree crown based on UAV visible images and OBIA-RF model: A case study for Chinese olive trees. Remote Sensing, 14(10), 2469. https://doi.org/10.3390/rs14102469.
• Yao, Y., & Wang, S. (2019). Evaluating the effects of image texture analysis on plastic greenhouse segments via recognition of the OSI-USI-ETA-CEI pattern. Remote Sensing, 11(3), 231.
• Yılmaz, V., Güngör, O., & Kadıoğulları, A. İ. (2015). Görüntü işleme teknikleri ile ağaçların boy, tepe çapı ve tepe hacimlerinin belirlenmesi. In TUFUAB VIII. Teknik Sempozyumu.
• Yiğit, A. Y., & Uysal, M. (2020). Automatic road detection from orthophoto images. Mersin Photogrammetry Journal, 2(1), 10–17.
• Yuan, X., Li, S., Chen, J., Yu, H., Yang, T., Wang, C., ... & Ao, X. (2024). Impacts of global climate change on agricultural production: A comprehensive review. Agronomy, 14(7), 1360.
• Zahoor, Z., Latif, M. I., Khan, I., & Hou, F. (2022). Abundance of natural resources and environmental sustainability: The roles of manufacturing value-added, urbanization, and permanent cropland. Environmental Science and Pollution Research, 29(54), 82365–82378.
• Zhang, H., Wang, L., Tian, T., & Yin, J. (2021). A review of unmanned aerial vehicle low-altitude remote sensing (UAV-LARS) use in agricultural monitoring in China. Remote Sensing, 13(6), 1221.