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All issues Volume 590 (2024) E3S Web of Conf., 590 (2024) 03004 References
Open Access
Issue
E3S Web of Conf.
Volume 590, 2024
6th Annual International Scientific Conference on Geoinformatics - GI 2024: “Sustainable Geospatial Solutions for a Changing World”
Article Number 03004
Number of page(s) 15
Section GIS in Geodesy and Cartography
DOI https://doi.org/10.1051/e3sconf/202459003004
Published online 13 November 2024
  1. B. L. Gardner, et al., Handbook of Agricultural Economics: Agricultural Development: Farmers, Farm Production and Farm Markets. Vol. 3. (2001): Elsevier. [Google Scholar]
  2. P. E. a. S. Wing, Pakistan Economic Survey. (2023): Government of Pakistan, Finance Division, Economic Adviser's Wing. [Google Scholar]
  3. A. Azam and M. Shafique, Agriculture in Pakistan and its Impact on Economy. A Review. International Journal of Advanced Science and Technology, (2017). 103: p. 47-60. [Google Scholar]
  4. M. Azam and M. Khan, Significance of the sugarcane crops with special and ference to NWFP. Sarhad J. agric, (2010). 26(2): p. 289-295. [Google Scholar]
  5. U. Pervaiz, et al., An analysis of sugarcane production with reference to extension services in Union Council Malakandher-Peshawar. Sarhad Journal of Agriculture, (2013). 29(1): p. 145-150. [Google Scholar]
  6. K. Sumesh, S. Ninsawat, and J. Som-Ard, Integration of RGB-based vegetation index, crop surface model and object-based image analysis approach for sugarcane yield estimation using unmanned aerial vehicle. Computers and Electronics in Agriculture, (2021). 180: p. 105903. [CrossRef] [Google Scholar]
  7. J. Chen, et al., Sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Computers and Electronics in Agriculture, (2021). 182: p. 105994. [CrossRef] [Google Scholar]
  8. Y.-K. Huang, et al., Color illustration of diagnosis and control for modern sugarcane diseases, pests, and weeds. (2018): Springer. [CrossRef] [Google Scholar]
  9. R. H. Cherry, Insect management in sugarcane. (2001): University of Florida Cooperative Extension Service, Institute of Food and …. [Google Scholar]
  10. K. Braithwaite, B. Croft, and R. Magarey. Progress in identifying the cause of Ramu stunt disease of sugarcane. in Proceedings of the Australian Society of Sugar Cane Technology. (2007). [Google Scholar]
  11. B. E. Wilson, Successful integrated pest management minimizes the economic impact of Diatraea saccharalis (Lepidoptera: Crambidae) on the Louisiana sugarcane industry. Journal of Economic Entomology, (2021). 114(1): p. 468-471. [CrossRef] [PubMed] [Google Scholar]
  12. H. Huang, et al., Detection of helminthosporium leaf blotch disease based on UAV imagery. Applied Sciences, (2019). 9(3): p. 558. [CrossRef] [Google Scholar]
  13. A. Vennila, et al., Partitioning of major nutrients and nutrient use efficiency of sugarcane genotypes. Sugar Tech, (2021). 23: p. 741-746. [CrossRef] [Google Scholar]
  14. S.-S. He, et al., Economic evaluation of water-saving irrigation practices for sustainable sugarcane production in Guangxi Province, China. Sugar Tech, (2021): p. 1-7. [Google Scholar]
  15. A. K. Verma, et al., Sugarcane yield forecasting model based on weather parameters. Sugar Tech, (2021). 23: p. 158-166. [CrossRef] [Google Scholar]
  16. M. Wang, et al., Mapping sugarcane in complex landscapes by integrating multitemporal Sentinel-2 images and machine learning algorithms. Land use policy, (2019). 88: p. 104190. [CrossRef] [Google Scholar]
  17. T. F. Canata, et al., Sugarcane yield mapping using high-resolution imagery data and machine learning technique. Remote Sensing, (2021). 13(2): p. 232. [CrossRef] [Google Scholar]
  18. H. Jiang, et al., Early season mapping of sugarcane by applying machine learning algorithms to Sentinel-1A/2 time series data: a case study in Zhanjiang City, China. Remote Sensing, (2019). 11(7): p. 861. [CrossRef] [Google Scholar]
  19. R. Singh, N. Patel, and A. Danodia, Mapping of sugarcane crop types from multi-date IRS-Resourcesat satellite data by various classification methods and field-level GPS survey. Remote Sensing Applications: Society and Environment, (2020). 19: p. 100340. [CrossRef] [Google Scholar]
  20. G. M. De Almeida, et al., Machine learning in the prediction of sugarcane production environments. Computers and Electronics in Agriculture, (2021). 190: p. 106452. [CrossRef] [Google Scholar]
  21. A. Nihar, N. Patel, and A. Danodia, Machine-Learning-Based Regional Yield Forecasting for Sugarcane Crop in Uttar Pradesh, India. Journal of the Indian Society of Remote Sensing, (2022). 50(8): p. 1519-1530. [CrossRef] [Google Scholar]
  22. A. Lonare, B. Maheshwari, and P. Chinnasamy, Village level identification of sugarcane in Sangali, Maharashtra using open source data. Journal of Agrometeorology, (2022). 24(3): p. 249-254. [CrossRef] [Google Scholar]
  23. W. Chivasa, O. Mutanga, and J. Burgueno, UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation. Computers and Electronics in Agriculture, (2021). 184: p. 106128. [CrossRef] [Google Scholar]
  24. M. R. Rahman, A. Islam, and M. A. Rahman, NDVI derived sugarcane area identification and crop condition assessment. Plan Plus, (2004). 1(2): p. 1-12. [Google Scholar]
  25. A. Imran, et al., Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass. Global Journal of Environmental Science and Management, (2020). 6(1): p. 97-108. [Google Scholar]
  26. B. F. T. Rudorff, et al. Temporal series of EVI/MODIS to identify land converted to sugarcane. in 2009 IEEE International Geoscience and Remote Sensing Symposium. (2009). IEEE. [Google Scholar]
  27. A. C. Xavier, et al., Multi‐temporal analysis of MODIS data to classify sugarcane crop. International Journal of Remote Sensing, (2006). 27(4): p. 755-768. [CrossRef] [Google Scholar]
  28. C. C. Júnior, et al., Sugarcane mapping in Paraná State Brazil using MODIS EVI images. International Journal of Advanced Remote Sensing and GIS, (2020). 9(1): p. 3205-3221. [CrossRef] [Google Scholar]
  29. K. Chandrasekar, et al., Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product. International Journal of Remote Sensing, (2010). 31(15): p. 3987-4005. [CrossRef] [Google Scholar]
  30. F. Xin, et al., Understanding the land surface phenology and gross primary production of sugarcane plantations by eddy flux measurements, MODIS images, and data-driven models. Remote Sensing, (2020). 12(14): p. 2186. [CrossRef] [Google Scholar]
  31. M. Zhu, et al. Studies on high-resolution remote sensing sugarcane field extraction based on deep learning. in IOP conference series: earth and environmental science. (2019). IOP Publishing. [Google Scholar]
  32. P. B. O. Statistics, HOUSEHOLDS, POPULATION, HOUSEHOLD SIZE AND ANNUAL GROWTH RATE, in 7th Population and Housing Census-2023 ‘The Digital Census’. (2023), Pakistan Bureau of Statistics. [Google Scholar]
  33. M. A. Khan, J. Zafar, and A. Bakhash, Energy requirement and economic analysis of sugarcane production in Dera Ismail Khan district of Pakistan. Gomal University Journal of Research, (2008). 24(1): p. 1-7. [Google Scholar]
  34. K. P. Bureau of Statistics Important District-Wise Socio-Economic Indicators of Khyber Pakhtunkhwa 2022. (2022), Bureau of Statistics , Khyber Pakhtunkhwa. [Google Scholar]
  35. T. Iffat, Desertification Dynamics and its Control Mechanisms in semi arid region of Pakistan: A case study of Karak district. (2012), Ph. D dissertation to the Institute of Geography, Urban and Regional …. [Google Scholar]
  36. A. Khaliq, et al., NOVEL HIGH CANE, SUGAR RESILIENT AND EARLY MATURING SUGARCANE VARIETY CPF 250 FOR PUNJAB PROVINCE. Life Science Journal, (2023). 20(8). [Google Scholar]
  37. P. Mafuratidze, et al., A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data. Geocarto International, (2023). 38(1): p. 2245788. [CrossRef] [Google Scholar]
  38. C. F. Brown, et al., Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, (2022). 9(1): p. 251. [CrossRef] [Google Scholar]
  39. V. Mehmood, et al. Time series-based active labeling framework for curating a multispectral sentinel 2 imagery dataset for crop type mapping. in IGARSS 2023- 2023 IEEE International Geoscience and Remote Sensing Symposium. (2023). IEEE. [Google Scholar]
  40. M. Lakshminarasimhappa, Web-based and smart mobile app for data collection: Kobo Toolbox/Kobo collect. Journal of Indian Library Association, (2022). 57(2): p. 72-79. [Google Scholar]
  41. K. K. Poloju, et al., New method of data collection using the kobo toolbox. Journal of Positive School Psychology, (2022): p. 1527-1535. [Google Scholar]
  42. C. A. Di Vittorio and A. P. Georgakakos, Land cover classification and wetland inundation mapping using MODIS. Remote Sensing of Environment, (2018). 204: p. 1-17. [CrossRef] [Google Scholar]
  43. T. J. Jackson, et al., Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, (2004). 92(4): p. 475-482. [CrossRef] [Google Scholar]
  44. X. Zhang, et al. Analysis of time-series modis 250M vegetation index data for vegetation classifiation in the wenquan area over the qinghai-tibet plateau. in 2010 IEEE International Geoscience and Remote Sensing Symposium. (2010). IEEE. [Google Scholar]
  45. C. J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, (1979). 8(2): p. 127-150. [CrossRef] [Google Scholar]
  46. A. Huete, et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, (2002). 83(1-2): p. 195-213. [CrossRef] [Google Scholar]
  47. N. Davidson and C. Finlayson, Earth observation for wetland inventory, assessment and monitoring. Aquatic Conservation: Marine and Freshwater Ecosystems, (2007). 17(3): p. 219-228. [CrossRef] [Google Scholar]
  48. S. Rahman and V. Mesev, Change vector analysis, tasseled cap, and NDVI-NDMI for measuring land use/cover changes caused by a sudden short-term severe drought: 2011 Texas event. Remote sensing, (2019). 11(19): p. 2217. [CrossRef] [Google Scholar]
  49. O. Strashok, M. Ziemiańska, and V. Strashok, Evaluation and Correlation of Sentinel-2 NDVI and NDMI in Kyiv (2017–2021). Journal of Ecological Engineering, (2022). 23(9). [Google Scholar]
  50. N. Inman-Bamber and D. M. Smith, Water relations in sugarcane and response to water deficits. Field crops research, (2005). 92(2-3): p. 185-202. [CrossRef] [Google Scholar]
  51. Z. S. Venter, et al., Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, (2022). 14(16): p. 4101. [CrossRef] [Google Scholar]
  52. R. G. Congalton and K. Green, Assessing the accuracy of remotely sensed data: principles and practices. (2019): CRC press. [CrossRef] [Google Scholar]
  53. M. Hassan, et al., Exploring the ratooning potential of sugarcane (Saccharum officinarum L.) genotypes under varying harvesting times of plant crop. Pakistan Journal of Agriculture Research, (2017). 30: p. 303-309. [Google Scholar]
  54. N. Farooq and S. H. Gheewala, Assessing the impact of climate change on sugarcane and adaptation actions in Pakistan. Acta Geophysica, (2020). 68: p. 1489-1503. [CrossRef] [Google Scholar]
  55. M. Pal, Random forest classifier for remote sensing classification. International journal of remote sensing, (2005). 26(1): p. 217-222. [CrossRef] [Google Scholar]

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