Remote sensing and GIS based crop monitoring: a case study of Tavra village in Vadodara

• Autorzy: Zalavadiya Umangkumar B. , Sabhadiya Himanshi R. , Kapupara Parth J.

Identyfikatory

DOI

10.57599/gisoj.2025.5.1.167

• Języki publikacji: EN

Abstrakty

EN

Food security is increasingly challenged by environmental changes, natural resource degradation, and population growth. Crop yields have already stagnated in many regions and are further affected by rising temperatures. The growing global population imposes a direct demand on agriculture to produce food, fiber, and fodder, necessitating the consumption of vast amounts of water. To maximize agricultural productivity and ensure sustainable crop yields, continuous crop monitoring is essential. Remote sensing has emerged as a powerful technology for vegetation monitoring, enabling spectral analysis of high-resolution satellite imagery to assess crop health and development. This study utilizes remote sensing techniques in conjunction with Geographic Information Systems (GIS) to monitor crop conditions. The Green Chromatic Coordinate (GCC) and Normalized Difference Vegetation Index (NDVI) were estimated using Landsat-9 satellite imagery. The analysis was conducted using QGIS for Tavra Village Farm, near Parul University, Waghodia, Vadodara, Gujarat, India. The observed GCC values ranged from 0.9352 to 0.3297, while NDVI values varied between 0.3300 and 0.0398 over the temporal period. The trend analysis of GCC and NDVI indicated an initial increase from November (early crop growth stage) to January (mid-growth stage), followed by a decline by February (crop maturity stage). These findings demonstrate the effectiveness of remote sensing and GIS in monitoring crop growth patterns, offering valuable insights for precision agriculture and resource management.

Słowa kluczowe

EN

crop monitoring; remote sensing; GIS; GCC; NDVI

PL

monitoring upraw; teledetekcja; GIS; GCC; NDVI

• Wydawca: SILGIS Association ; Croatian-Polish Scientific Network
• Czasopismo: GIS Odyssey Journal
• Rocznik: 2025
• Tom: Vol. 5, no. 1
• Strony: 167--175
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Zalavadiya Umangkumar B.

• Parul University, Department of Agricultural Engineering, Parul Institute of Technology, Vadodara- 391760, Gujarat, India

• https://orcid.org/0000-0003-0915-6805

autor

Sabhadiya Himanshi R.

• Parul University, Department of Agricultural Engineering, Parul Institute of Technology, Vadodara- 391760, Gujarat, India

• https://orcid.org/0000-0001-6270-6680

autor

Kapupara Parth J.

• Parul University, Department of Agricultural Engineering, Parul Institute of Technology, Vadodara- 391760, Gujarat, India

• https://orcid.org/0000-0003-4033-4892

Bibliografia

• 1. Bhandari A.K., Kumara A., Singh, G.K. (2012). Feature Extraction using Normalized Difference Vegetation Index (NDVI): a Case Study of Jabalpur City. Procedia Technology, 6, 612-621.
• 2. Dadhwal V.K. (2006). Crop growth, and productivity monitoring and simulation using remote sensing and GIS. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, pp. 263-289.
• 3. Gandhi M., Parthiban S., Thummalu N. Christy A. (2015). NDVI: Vegetation change detection using remote sensing and GIS - A case study of Vellore District. Procedia Computer Science, 57, 1199-1210.
• 4. Hunt E.R., Hively W.D., Fujikava S.J., Linden D.S., Daughtry C.S.T., McCarty G.W. (2010). Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sensing, 2, 290-305.
• 5. Infantial J.R., Anitha D. (2017). Agriculture Field Monitoring using Wireless Sensor Networks to Improving Crop Production, International Journal of Engineering Science and Computing, 7, 5216-5221.
• 6. Jongmin K., Youngryel R., Chongya J., Yorum H. (2019). Continuous observation of vegetation canopy dynamic using an integrated low cost, near-surface remote sensing. Agricultural and forest meteorology, 264, 164-177.
• 7. Khabbazan S., Vermunt P., Steele-Dunne S., Arntz L.R., Marinetti C., Valk D.V., Iannini L., Molijn R., Westerdijk K., Sande C.V. (2019). Crop Monitoring using Sentinel-1 Data: A Case Study from the Netherlands. Remote Sensing, 11, 1887. DOI: 10.3390/rs11161887.
• 8. Koen H., Eli K.M., Michael L.M. (2018). Monitoring crop phenology using smartphone based near-surface remote sensing approach. Agricultural and forest metrology, 265, 327-337.
• 9. Li M., Shamshiri R.R., Weltzien C., Schirrmann M. (2022). Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany. Remote Sensing, 14, 4426.
• 10. Mamatkulov Z., Safarov E., Oymatov R., Abdurahmanov I., Rajapbaev M. (2021). Application of GIS and RS in real time crop monitoring and yield forecasting: a case study of cotton fields in low and high productive farmlands. E3S Web Conferences, 227, 03001. DOI:10.1051/e3sconf/202122703001.
• 11. Mishra A., Parikh A. (2017). Crop health monitoring informatics system based on IoT. International journal for scientific research and development. National Conference on Advances in Computer Science Engineering & Technolog, pp. 15-20.
• 12. Norasma C.Y., Abu Sari M.Y., Fadzilah M.A., Ismail M.R., Omar M.H., Zulkarami B., Hassim Y.M., Tarmidi Z. (2018). Rice crop monitoring using multirotor UAV and RGB digital camera at early stage of growth. IOP Conference Series: Earth and Environmental Science, vol. 169. DOI 10.1088/1755-1315/169/1/012095.
• 13. Rahman Md.R., Islam A. H. Md. H., Rahman M.A. (2004). NDVI derived sugarcane area identification and crop condition assessment. Plan Plus, vol. 1, no. 2.
• 14. Sakthipriya N. (2014). An Effective Method for Crop Monitoring Using Wireless Sensor Network. Middle-East Journal of Scientific Research, 20(9), 1127-1132. DOI:10.5829/idosi.mejsr.2014.20.09.114152.
• 15. Shanmugapriya P., Rathika S., Ramesh T., Janaki P. (2019). Applications of Remote Sensing in Agriculture. International Journal of Current Microbiology and Applied Sciences, vol. 8, no. 1, 2319-7706.
• 16. Singhal G., Bansod B., Mathew L. (2018). Real time crop health monitoring using remote sensing and ancillary information using GIS. 19th Esri India User Conference.
• 17. Torres-Sanchez J., Pena J.M., de Castro A.I., Lopez-Granados F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, vol. 103, 104-113. https://doi.org/10.1016/j.compag.2014.02.009.
• 18. Toshihiro S., Antoly A.G., Anthony L.N., Timothy J.A. (2011). An alternative method using digital cameras for continuous monitoring of crop status. Agricultural and Forest Meteorology, vol. 154-155, pp. 113-126.
• 19. Vyas V.S., Gadhiya R.B., Radadiya S.K. (2020). Crop Monitoring Using Near Surface Remote Sensing Approach. Bachelor of Technology (Agricultural Engineering) Thesis (Unpublished). Junagadh Agricultural University, Junagadh.
• 20. Zalavadiya U.B., Nebhnani V.M., Kaur I. (2019). Estimation of Soil Moisture using Remote Sensing. Bachelor of Technology (Agricultural Engineering) Thesis (Unpublished). Junagadh Agricultural University, Junagadh.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).