Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 12 No. 2 (2025)

Mapping of mango cropping area using machine learning techniques in Tamil Nadu

DOI
https://doi.org/10.14719/pst.8760
Submitted
8 April 2025
Published
24-05-2025 — Updated on 09-06-2025
Versions

Abstract

Mango area mapping is crucial for forecasting production prior to harvest and developing policies that ensure food and nutritional security. This study focuses on the integration of remote sensing and machine learning algorithms for real time prediction of mango cropped area in Krishnagiri and Dharmapuri districts of Tamil Nadu state in India. The cloud free images of Sentinel 2 were acquired corresponding to the fruit setting stage of the crop during the main crop season (January to June 2024) and pre-processed in ArcGIS 10.8 software. Supervised classification was carried out using eCognition Developer 10.3 software by combining object-based image analysis (OBIA) and Random Forest algorithm. Based on the analysis, the area under mango cultivation in Krishnagiri district was 31824.22 hectares, showing a 1.5 % deviation from the Department of Economics and Statistics data. The overall classification accuracy was 0.85, with a kappa index of 0.70. In Dharmapuri district, the mango area was slightly overestimated (14950.24 ha) compared to the government data (14589 ha), with a percent deviation of -2.5 %. The overall classification accuracy was 0.88, with a kappa index was 0.76. Accurate mango area mapping supports precision agriculture, efficient resource management and yield optimization, thereby aiding farmers, scholars and policymakers in informed decision-making.

References

  1. 1. Indiastat. Selected state-wise Area, production and productivity of mango in India. 2025 Feb 21.
  2. 2. Craig M, AtkinsonD. A literature review of crop area estimation. 2013, UN-FAO, Rome, Italy.
  3. 3. Kaliaperumal R, Pazhanivelan S, Kumaraperumal R, Mugilan GR. Mapping mango area using multi-temporal feature extraction from Sentinel 1A SAR data in Dharmapuri, Krishnagiri and Salem districts of Tamil Nadu. Madras Agric J. 2019;106:647-51. https://doi.org/10.29321/MAJ.2019.000325
  4. 4. Paul NC, Sahoo PM, Ahmad T, Sahoo, RN, Krishna G, Lal SB. Acreage estimation of mango orchards using hyperspectral satellite data. Indian J Hortic. 2018;75(1):27-33. https://doi.org/10.5958/0974-0112.2018.00005.1
  5. 5. Verma HC, Ahmed T, Rajan S. Mapping and area estimation of mango orchards of Lucknow region by applying knowledge based decision tree to Landsat 8 OLI Satellite Images. Int J Innov Technol Explor Eng. 2020;9(3):3627-35. https://doi.org/10.35940/ijitee.B8109.019320
  6. 6. Luo HX, Dai SP, Li MF, Liu EP, Zheng Q, Hu YY, Yi XP. 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
  7. 7. Roy S, More R, Kimothi MM, Mamatha S, Vyas, SP, Ray SS, Comparative analysis of object based and pixel-based classification for mapping of mango orchards in Sitapur district of Uttar Pradesh. J Geom. 2018;12(1):1-8.
  8. 8. 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):811-18. https://doi.org/10.9734/ijecc/2023/v13i92302
  9. 9. Anderson NT, Underwood JP, Rahman MM, Robson A, Walsh KB. Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery. Precis Agric. 2019;20:823-39. https://doi.org/10.1007/s11119-018-9614-1
  10. 10. Balakrishna B, Harshitha SN, Sankaran M, Rupa TR, Verma N. Development of standard protocols for assessment of cropped area using Sentinel 2 MSI (Spatial Resolution: 10 m) in mango. Prog Hortic. 2023;55(2):127-33. http://dx.doi.org/10.5958/2249-5258.2023.00019.2
  11. 11. Sabthapathy M, Kaliaperumal R, Pazhanivelan S, Velmurugan S. Cashew area mapping using Sentinel-2 in Ariyalur District of Tamil Nadu, India. Eco Env Cons. 2022;28:S512-S16. http://doi.org/10.53550/EEC.2022.v28i01s.068
  12. 12. Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens. 2004;58(3-4):239-58.https://doi.org/10.1016/j.isprsjprs.2003.10.002
  13. 13. Stephen S, Haldar D, Patel NR. Impact of various vegetation indices on mango orchard mapping using object-based image analysis. J Geom. 2022;16(2):159-66. https://doi.org/10.58825/jog.2022.16.2.45
  14. 14. Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M. Sentinel-2 data for land cover/use mapping: A review. Remote Sens. 2020;12(14):2291. https://doi.org/10.3390/rs12142291
  15. 15. Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ. 1991;37(1):35-46. https://doi.org/10.1016/0034-4257(91)90048-B
  16. 16. Raju C, Ajith K, Ajithkumar B, Anitha S, Vijayan VD. Rice area mapping in Palakkad district of Kerala using Sentinel-2 data and Geographic information system technique. J Appl Nat Sci. 2022;14(4):1360. https://doi.org/10.31018/jans.v14i4.3898
  17. 17. Pal S, Pandey SK, Sharma SK, Nair R, Acreage estimation of mango orchards in Jabalpur district of Madhya Pradesh using Sentinel-2 imagery. Eco Env Cons. 2023;29(2):840-44. http://doi.org/10.53550/EEC.2023.v29i02.050
  18. 18. Chabalala Y, Adam E, Ali KA. Machine learning classification of fused Sentinel-1 and Sentinel-2 image data towards mapping fruit plantations in highly heterogenous landscapes. Remote Sens. 2022;14(11):2621. https://doi.org/10.3390/rs14112621
  19. 19. Nabil M, Farg E, Arafat SM, Aboelghar M, Afify NM, Elsharkawy MM. Tree-fruits crop type mapping from Sentinel-1 and Sentinel-2 data integration in Egypt's New Delta project. Remote Sens Appl Soc Environ. 2022;27:100776. https://doi.org/10.1016/j.rsase.2022.100776

Downloads

Download data is not yet available.