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Plant Science Today (PST)

Research Articles

Early Access

Advanced mapping of mango orchards in Chittoor district through object-based image analysis approach using Sentinel-2 imagery

DOI
https://doi.org/10.14719/pst.6889
Submitted
24 December 2024
Published
10-05-2025
Versions

Abstract

Mango is crucial in India's agricultural economy, particularly in the Chittoor District of Andhra Pradesh. As mango cultivation transitions from subsistence farming to large-scale commercial operations, accurate mapping and monitoring of mango plantations are essential for sustainable agricultural management. This study explored the application of Object-Based Image Analysis (OBIA) using high-resolution Sentinel-2 satellite imagery for mapping mango plantations in Chittoor District. OBIA, a more advanced approach than traditional pixel- based methods, integrates spectral, spatial and contextual data, enabling the identification of mango orchards with higher accuracy. Sentinel-2 multispectral bands were utilized to distinguish mango plantations based on canopy density, inter-row spacing and orchard layout. Ground-truth data collected from 531 points across the district validated the classification process. The results show that OBIA achieved an overall accuracy of 87.0 % with a Kappa index of 0.74, signifying strong agreement with the ground truth data and the total mango area mapped in Chittoor District is 97,006 hectares. This study highlights the effectiveness of OBIA for accurate mango area estimation and suggests potential improvements, including the integration of hyperspectral data and advanced algorithms. This study offers valuable insights for agricultural management, resource optimization and policy planning, with implications for broader crop mapping and precision agriculture applications.

References

  1. Bhole V, Kumar A. Mango quality grading using deep learning technique: Perspectives from agriculture and food industry. Proceed Info Tech Edu; 2020. https://doi.org/10.1145/3368308.3415370
  2. Indiastat. Area, production and yield of principal crops. In: Department of Agriculture and Cooperation and Department of Economics, Statistics Division, Government of India, New Delhi; 2021.
  3. Ramteke I, Rajankar P, Reddy GO, Kolte D, Sen T. Optical remote sensing applications in crop mapping and acreage estimation: A review. Inte J Ecol Environ Sci. 2020;2(4):696–703.
  4. Kalaivani A, Khilar R. Crop classification and mapping for agricultural land from satellite images. In: Hemanth D, editor. Artificial Intelligence Techniques for Satellite Image Analysis. Remote Sensing and Digital Image Processing, vol 24. Cham: Springer; 2020. p. 213–33. https://doi.org/10.1007/978-3-030-24178-0_10
  5. Roy S, More R, Kimothi M, Mamatha S, Vyas S, Ray S. Comparative analysis of object-based and pixel-based classification for mapping of mango orchards in Sitapur district of Uttar Pradesh. J Geomat. 2018;12(1):1–8.
  6. Zhai D, Dong J, Cadisch G, Wang M, Kou W, Xu J, et al. Comparison of pixel- and object-based approaches in phenology-based rubber plantation mapping in fragmented landscapes. Remote Sens. 2017;10(1):44.
  7. https://doi.org /10.3390/rs10010044
  8. Berhane TM, Lane CR, Wu Q, Anenkhonov OA, Chepinoga VV, Autrey BC, et al. Comparing pixel- and object-based approaches in effectively classifying wetland-dominated landscapes. Remote Sens. 2017;10(1):46. https://doi.org/
  9. 3390/rs10010046
  10. Harish M, Kaliaperumal R, Pazhanivelan S, Sivakumar C, Kumaraperumal R. Mango area mapping using very high-resolution satellite data in major blocks of Krishnagiri District, Tamil Nadu, India. Int J Environ Clim Change. 2023;13(9):811–8. https://doi.org/10.9734/ijecc/2023/v13i92302
  11. Aggarwal N, Srivastava M, Dutta M. Comparative analysis of pixel-based and object-based classification of high-resolution remote sensing images–A review. Int J Eng Trends Technol. 2016;38(1):5–11. https://doi.org/10.14445/
  12. /IJETT-V38P202.
  13. Gao Y, Mas JF. A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. Online J Earth Sci. 2008;2(1):27–35.
  14. Yadav S, Rizvi I, Kadam S. Comparative study of object-based image analysis on high-resolution satellite images for urban development. Int J Tech Res Appl. 2015;31:105–10.
  15. Zheng C, Chen P, Pang J, Yang X, Chen C, Tu S. A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard. Biosys Eng. 2021;206:32–54. https://doi.org/10.1016/j.
  16. biosystemseng.2021.03.012
  17. Afsar MM, Bakhshi AD, Iqbal MS, Hussain E, Iqbal J. High-precision mango orchard mapping using a deep learning pipeline leveraging object detection and segmentation. Remote Sens. 2024;16(17):3207. https://doi.org/10.3390/
  18. rs16173207
  19. Luo HX, Dai SP, Li MF, Liu EP, Zheng Q, Hu YY, et al. Comparison of machine learning algorithms for mapping mango plantations based on Gaofen-1 imagery. J Integr Agric. 2020;19(11):2815–28. https://doi.org/10.1016/S2095-3119(20)63208-7
  20. Luo H, Li M, Dai S, Li H, Li Y, Hu Y, et al. Combinations of feature selection and machine learning algorithms for object-oriented betel palms and mango plantations classification based on Gaofen-2 imagery. Remote Sens.
  21. ;14(7):1757. https://doi.org/10.3390/rs14071757
  22. Stephen S, Haldar D. Categorisation of mango orchard age groups using object-based image analysis. Arab J Geosci. 2024;17(2):62. https://doi.org/10.1007/s12517-024-11857-z
  23. Arcila-Diaz L, Mejia-Cabrera HI, Arcila-Diaz J, editors. Estimation of mango fruit production using image analysis and machine learning algorithms. Informatics. 2024;11(4):87. https://doi.org/10.3390/informatics11040087
  24. Wang Z, Underwood J, Walsh KB. Machine vision assessment of mango orchard flowering. Comput Electron Agric. 2018;151:501–11. https://doi.org/10.1016/j.compag.2018.06.040
  25. Neupane C, Koirala A, Walsh KB. In-orchard sizing of mango fruit: 1. Comparison of machine vision-based methods for on-the-go estimation. Hortic. 2022;8(12):1223. https://doi.org/10.3390/horticulturae8121223
  26. Torgbor BA, Rahman MM, Brinkhoff J, Sinha P, Robson A. Integrating remote sensing and weather variables for mango yield prediction using a machine learning approach. Remote Sens. 2023;15(12):3075. https://doi.org/10.3390
  27. /rs15123075
  28. Tian Y, Yang C, Huang W, Tang J, Li X, Zhang Q. Machine learning-based crop recognition from aerial remote sensing imagery. Front Earth Sci. 2021;15:54–69. https://doi.org/10.1007/s11707-020-0861-x

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