Advancements in remote sensing based crop yield modelling in India
DOI:
https://doi.org/10.54386/jam.v25i3.2316Keywords:
Crop yield, Remote Sensing, Machine learning, semi-physical model, assimilationAbstract
Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.
References
Ayyangar, R. S., Rao, P. N., & Rao, K. R. (1980). Crop cover and crop phenological information from red and infrared spectral responses. J. Indian Soc. Remote Sens., 8, 23-29.
Bhattacharya, B. K., Mallick, K., Nigam, R., Dakore, K., & Shekh, A. M. (2011). Efficiency based wheat yield prediction in a semi-arid climate using surface energy budgeting with satellite observations. Agric. For. Meteorol., 151(10), 1394-1408.
Chakraborty, D., Sehgal, V. K., Ray, M., Dhakar, R., Sahoo, R. N., Das, D. K., & Kumar, P. (2018). District-wise statistical yield modelling of wheat using weather and remote sensing inputs.
Chaudhari, K. N., Tripathy, R., & Patel, N. K. (2010). Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data. J. Agrometeorol., 12(2), 174-180.
Chaurasiya, G., Saxena, S., Tripathy, R., Chaudhari, K. N., & Ray, S. S. (2017). Semi physical approach for sugarcane yield modelling with remotely sensed inputs. Vayu Mandal, 43(1), 11-22.
Dadhwal, V. K., & Bhat, Y. (2023). Revisiting statistical spectral-agrometeorological wheat yield models for Punjab using MODIS EVI and NCMRWF re-analysis temperature data. J. Agrometeorol., 25(1), 10-17.
Das, A., Kumar, M., Kushwaha, A., Dave, R., Dakhore, K. K., Chaudhari, K., & Bhattacharya, B. K. (2023). Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing. Remote Sens. Appl.: Soc. Environ., 30, 100962.
Dhakar, R., Sehgal, V. K., Chakraborty, D., Sahoo, R. N., Mukherjee, J., Ines, A. V., & Roy, S. B. (2022). Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing. Agric. Syst., 195, 103299.
Dubey, S. K., Gavli, A. S., Yadav, S. K., Sehgal, S., & Ray, S. S. (2018). Remote sensing-based yield forecasting for sugarcane (Saccharum officinarum L.) crop in India. J. Indian Soc. Remote Sens., 46, 1823-1833.
Gangopadhyay, P. K., Shirsath, P. B., Dadhwal, V. K., & Aggarwal, P. K. (2022). A new two-decade (2001–2019) high-resolution agricultural primary productivity dataset for India. Sci. Data, 9(1), 730.
Gontia, N. K., & Tiwari, K. N. (2011). Yield estimation model and water productivity of wheat crop (Triticum aestivum) in an irrigation command using remote sensing and GIS. J. Indian Soc. Remote Sens., 39, 27-37.
Gumma, M. K., Kadiyala, M. D. M., Panjala, P., Ray, S. S., Akuraju, V. R., Dubey, S., & Whitbread, A. M. (2022). Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. J. Indian Soc. Remote Sens., 50(2), 257-270.
Gupta, S., Vashisth, A., Krishnan, P., Lama, A., Prasad, S., & Aravind, K. S. (2022). Multistage wheat yield prediction using hybrid machine learning techniques. J. Agrometeorol., 24(4), 373-379.
Hoogenboom, G., White, J. W., & Messina, C. D. (2004). From genome to crop: integration through simulation modeling. Field Crops Res., 90(1), 145-163.
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., & Ritchie, J. T. (2003). The DSSAT cropping system model. Eur. J. Agron., 18(3-4), 235-265.
Khose, S. B., Mailapalli, D. R., Biswal, S., & Chatterjee, C. (2022). UAV-based multispectral image analytics for generating crop coefficient maps for rice. Arab. J. Geosci., 15(22), 1681.
Kirthiga, S. M., & Patel, N. R. (2022). In-season wheat yield forecasting at high resolution using regional climate model and crop model. AgriEngi., 4(4), 1054-1075.
Krupavathi, K., Raghubabu, M., Mani, A., Parasad, P. R. K., & Edukondalu, L. (2022). Field-scale estimation and comparison of the sugarcane yield from remote sensing data: A machine learning approach. J. Indian Soc. Remote Sens., 1-14.
Kumar, M., Das, A., Chaudhari, K. N., Dutta, S., Dakhore, K. K., & Bhattacharya, B. K. (2022). Field-scale assessment of sugarcane for mill-level production forecasting using Indian satellite data. J. Indian Soc. Remote Sens., 50(2), 313-329.
Kumar, D. A., Neelima, T. L., Srikanth, P., Devi, M. U., Suresh, K., & Murthy, C. S. (2022). Maize yield prediction using NDVI derived from Sentinal 2 data in Siddipet district of Telangana state. J. Agrometeorol., 24(2), 165-168.
Milesi, C., & Kukunuri, M. (2022). Crop yield estimation at gram panchayat scale by integrating field, weather and satellite data with crop simulation models. J. Indian Soc. Remote Sens., 50(2), 239-255.
Mohite, J., Sawant, S., Sakkan, M., Shivalli, P., Kodimela, K., & Pappula, S. (2019, July). Spatialization of rice crop yield using sentinel-1 sar and oryza crop growth simulation model. In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-6). IEEE.
Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol., 9(3), 747-766.
Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical transactions of the royal society of London. B, Biological Sciences, 281(980), 277-294.
Moulin, S., Bondeau, A., & Delecolle, R. (1998). Combining agricultural crop models and satellite observations: from field to regional scales. Int. J. Remote Sens., 19(6), 1021-1036.
Navalgund, R. R., & Kasturirangan, K. (1983). The Indian remote sensing satellite: a programme overview. Proc. Indian Acad. Sci. Section C: Engi. Sci., 6, 313-336.
Nihar, A., Patel, N. R., & Danodia, A. (2022). Machine-Learning-Based Regional Yield Forecasting for Sugarcane Crop in Uttar Pradesh, India. J. Indian Soc. Remote Sens., 50(8), 1519-1530.
Nihar, A., Patel, N. R., Singh, R., & Danodia, A. (2023). Satellite-Based Light Use Efficiency Model for Sugarcane Yield Estimation at Field Scale. J. Indian Soc. Remote Sens., 51(6), 1357-1369.
Palanisamy, S., Latha, K. R., Pazhanivalen, S., Ramalingam, K., Karthikeyan, G. & Sundarmanian, N. S. (2023).Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices. Curr. Sci., 123(12):1473-1480
Pandey, S., Patel, N. R., Danodia, A., & Singh, R. (2019). Discrimination of sugarcane crop and cane yield estimation using Landsat and IRS resourcesat satellite data. The Int. Arch. Photogramm., 42, 229-233.
Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric., 121, 57-65.
Prasad, N. R., Patel, N. R., & Danodia, A. (2021). Crop yield prediction in cotton for regional level using random forest approach. Spat. Inf. Res., 29, 195-206.
Prasad, N. R., Patel, N. R., Danodia, A., & Manjunath, K. R. (2022). Comparative performance of semi-empirical based remote sensing and crop simulation model for cotton yield prediction. Model. Earth Syst.Environ., 8(2), 1733-1747.
Rajasivaranjan, T., Anandhi, A., Patel, N. R., Irannezhad, M., Srinivas, C. V., Veluswamy, K., & Raja, P. (2022). Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield. Scien Rep., 12(1), 16985.
Rakhee, Singh, A., & Mittal, M. (2022). Rice yield prediction using hybrid PSACO algorithm based on back-propagation neural network. J. Agrometeorol, 24(1), 109-112.
Sahai, B., & Dadhwal, V. K. (1990). Remote sensing in agriculture. In J. P. Verma & A. Verma (Eds.), Technology blending and agrarian prosperity (pp. 83–98). Malhotra Publishing House.
Sahai, B., Chandrashekar, S., Barde, N. K., & Bhushana, S. (1976). Agricultural Resources Inventory and Survey Experiment. In COSPAR, Plenary Meeting, 19 th, Philadelphia, Pa (p. 1976).
Sahai, B., Dadhwal, V. K., & Chakraborty, M. (1989). Comparison of SPOT, TM and MSS data for agricultural land-use mapping in Gujarat (India). Acta Astronaut., 19(6-7), 505-511.
Shanmugapriya, P., Latha, K. R., Pazhanivelan, S., Kumaraperumal, R., Karthikeyan, G., & Sudarmanian, N. S. (2022). Cotton yield prediction using drone derived LAI and chlorophyll content. J. Agrometeorol., 24(4), 348-352.
Sinclair, T. R., & Horie, T. (1989). Leaf nitrogen, photosynthesis, and crop radiation use efficiency: a review. Crop Sci., 29(1), 90-98.
Tripathy, R., Chaudhari, K. N., Bairagi, G. D., Pal, O., Das, R., & Bhattacharya, B. K. (2021). Towards fine-scale yield prediction of three major crops of India using data from multiple satellite. J. Indian Soc. Remote Sens., 1-14.
Tripathy, R., Nigam, R., & Bhattacharya, B. K. (2023). Agrometeorological Approach for Sugarcane Yield Estimation at Regional Scale Using Satellite Remote Sensing. J. Indian Soc. Remote Sens., 1-14.
Verma, A. K., Garg, P. K., Hari Prasad, K. S., & Dadhwal, V. K. (2020). Modelling of sugarcane yield using LISS-IV data based on ground LAI and yield observations. Geocarto Int., 35(8), 887-904.
Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ., 236, 111402.
Whitcraft, A. K., Becker-Reshef, I., Justice, C. O., Gifford, L., Kavvada, A., & Jarvis, I. (2019). No pixel left behind: Toward integrating Earth Observations for agriculture into the United Nations Sustainable Development Goals framework. Remote Sens. Environ., 235, 111470.
Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., ... & Loupian, E. (2023). Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4), nwac290.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 N. R. PATEL, SHWETA POKHARIYAL, R. P. SINGH
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is a human-readable summary of (and not a substitute for) the license. Disclaimer.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
