Abstract
Early and precise estimation of crop yield plays a crucial role in quantitative and financial assessment at field level, to lay down strategic plans for import–export policies for agricultural commodities and to doubling the income of farmers. In this study, a machine learning based random forest (RF) algorithm was used to predicate cotton yield at three distinct times before the actual harvest in the state of Maharashtra in India using R package. Long-term agromet-spectral variables derived from multi-sensor satellites with actual crop yield from 2001 to 2017, were used to generate co-linearity of predictor variables and further, calibrate and validate the RF model. The performance of the RF model was found reliable and faster in predicting the crop yield with the most influencing variables with 69%, 60% and 39% of coefficient of determination (R2) in the final yield for September, December and February months, respectively using CART decision tree and recursive feature elimination method in R programming. Results showed as RF algorithm has the capability to integrate and process a large number of inputs as derived from different satellite modalities, unscaled and non-uniform ground based information, expert knowledge, etc. With high precision and avoid over fitting of the model.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs41324-020-00346-6/MediaObjects/41324_2020_346_Fig7_HTML.png)
Similar content being viewed by others
References
Bender, S. F., & van der Heijden, M. G. A. (2015). Soil biota enhance agricultural sustainability by improving crop yield, nutrient uptake and reducing nitrogen leaching losses. Journal of Applied Ecology, 52, 228–239. https://doi.org/10.1111/1365-2664.12351.
Molden, D., Lautze, J., Shah, T., Bin, D., Giordano, M., & Sanford, L. (2010). Governing to grow enough food without enough water—secondbest solutions show the way. International Journal of Water Resources Development, 26(2), 249–263.
FAO. (2016). Sustainable development goals | Food and Agriculture Organization of the United Nations. Retrieved May 10, 2020, from http://www.fao.org/sustainable-development-goals/en/.
Jones, J. W., Antle, J. M., Basso, B., et al. (2017). Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systems, 155, 269–288. https://doi.org/10.1016/j.agsy.2016.09.021.
Basso, B., Cammarano, D., & Carfagna, E. (2013, July). Review of crop yield forecasting methods and early warning systems. In Proceedings of the first meeting of the scientific advisory committee of the global strategy to improve agricultural and rural statistics, FAO Headquarters, Rome, Italy (Vol. 41).
Johnston, A. E., Poulton, P. R., White, R. P., & Macdonald, A. J. (2016). Determining the longer term decline in plant-available soil phosphorus from short-term measured values. Soil Use and Management, 32, 151–161.
Frieler, K., Schauberger, B., Arneth, A., et al. (2017). Earth’ s future special section: Understanding the weather signal in national crop-yield variability. Earth’ s Future. https://doi.org/10.1002/eft2.217.
Leng, G., Zhang, X., Huang, M., et al. (2016). The role of climate covariability on crop yields in the conterminous United States. Scientific Reports. https://doi.org/10.1038/srep33160.
Ma, B., & Bruno, B. (2018). Drivers of within-field spatial and temporal variability of crop yield across the US Midwest. Scientific Reports. https://doi.org/10.1038/s41598-018-32779-3.
Hoffmann, H., Zhao, G., Asseng, S., et al. (2016). Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS ONE. https://doi.org/10.1371/journal.pone.0151782.
Fang, H., Liang, S., & Hoogenboom, G. (2011). Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. International Journal of Remote Sensing, 32, 1039–1065. https://doi.org/10.1080/01431160903505310.
Li, Z., Wang, J., Xu, X., et al. (2014). Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation. Ecological Modelling, 291, 15–27. https://doi.org/10.3390/rs70912400.
Li, Y., Zhou, Q., Zhou, J., et al. (2014). Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions. Ecological Modelling, 291, 15–27. https://doi.org/10.1016/j.ecolmodel.2014.07.013.
Jackson, R. D., Idso, S. B., Reginato, R. J., & Pinter, P. J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133–1138. https://doi.org/10.1029/WR017i004p01133.
Kustas, W. P., & Norman, J. M. (2000). A two-source energy balance approach using directional radiometric temperature observations for sparse canopy covered surfaces. Agronomy Journal, 92(5), 847–854.
Hoogenboom, G. (2000). Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology, 103, 137–157.
Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150, 1443–1452.
Zhang, J., Tian, H., Yang, J., & Pan, S. (2018). Improving representation of crop growth and yield in the dynamic land ecosystem model and its application to China. Journal of Advances in Modeling Earth Systems, 10, 1680–1707. https://doi.org/10.1029/2017MS001253.
Gerssen-Gondelach, S., Wicke, B., & Faaij, A. (2015). Assessment of driving factors for yield and productivity developments in crop and cattle production as key to increasing sustainable biomass potentials. Food Energy Security, 4, 36–75. https://doi.org/10.1002/FES3.53.
Supit, I., van Diepen, C. A., De Wit, A. J. W., et al. (2012). Assessing climate change effects on European crop yields using the crop growth monitoring system and a weather generator. Agricultural and Forest Meteorology, 164, 96–111. https://doi.org/10.1016/j.agrformet.2012.05.005.
Hatfield, J. L., & Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Weather and CLIMATE Extremes, 10, 4–10. https://doi.org/10.1016/j.wace.2015.08.001.
van Bussel, L. G. J., Müller, C., van Keulen, H., et al. (2011). The effect of temporal aggregation of weather input data on crop growth models’ results. Agricultural and Forest Meteorology, 151, 607–619. https://doi.org/10.1016/j.agrformet.2011.01.007.
Verma, U., Piepho, H. P., Ogutu, J. O., et al. (2014). Development of zonal agromet models for district-level cotton yield forecasts in Haryana State, India development of zonal agromet models for district-level cotton yield forecasts in Haryana State, India. International Journal of Agricultural and Statistical Sciences, 10(1), 59–65.
Pinke, Z., & Lövei, G. L. (2017). Increasing temperature cuts back crop yields in Hungary over the last 90 years. Global Change Biology, 23, 5426–5435.
Kern, A., Barcza, Z., Marjanovi, H., et al. (2018). Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology, 261, 300–320. https://doi.org/10.1016/j.agrformet.2018.06.009.
Setiyono, T., Nelson, A., Holecz, F. (2014). Remote sensing based crop yield monitoring and forecasting. Expert Meeting on Crop Monitoring for Improved Food Security. http://www.fao.org/fileadmin/templates/rap/files/Project/Expert_Meeting__17Feb2014_/P2-4_Setiyono_2014_Remote-Sensing_based_Crop_Yield_Monitoring.pdf.
Huang, J., Gómez-Dans, J. L., Huang, H., et al. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276–277, 107609. https://doi.org/10.1016/j.agrformet.2019.06.008.
Doraiswamy, P. C., Moulin, S., Cook, P. W., & Stern, A. (2003). Crop yield assessment from remote sensing. Photogrammetric Engineering & Remote Sensing, 69, 665–674.
Zhao, D., Huang, L., Li, J., & Qi, J. (2007). A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 25–33. https://doi.org/10.1016/j.isprsjprs.2007.01.003.
Sakamoto, T., Gitelson, A. A., & Arkebauer, T. J. (2013). MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sensing of Environment, 131, 215–231. https://doi.org/10.1016/j.rse.2012.12.017.
Prasad, A. K., Chai, L., Singh, R. P., & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8, 26–33.
Gusso, A., Ducati, J. R., Veronez, M. R., et al. (2013). Spectral model for soybean yield estimate using MODIS/EVI data. International Journal of Geosciences, 04, 1233–1241. https://doi.org/10.4236/ijg.2013.49117.
Petersen, L. (2018). Real-time prediction of crop yields from MODIS relative vegetation health: A continent-wide analysis of Africa. Remote Sensing, 10, 1726. https://doi.org/10.3390/rs10111726.
Landau, S., Mitchell, R. A. C., Barnett, V., et al. (2000). A parsimonious, multiple-regression model of wheat yield response to environment. Agricultural and Forest Meteorology, 101, 151–166. https://doi.org/10.1016/S0168-1923(99)00166-5.
Sheehy, J. E., Mitchell, P. L., & Ferrer, A. B. (2006). Decline in rice grain yields with temperature: Models and correlations can give different estimates. Field Crops Research, 98, 151–156. https://doi.org/10.1016/j.fcr.2006.01.001.
Cooner, A. J., Shao, Y., & Campbell, J. B. (2016). Detection of urban damage using remote sensing and machine learning algorithms: Revisiting the 2010 Haiti earthquake. Remote Sensing. https://doi.org/10.3390/rs8100868.
Lawler, A. J. D. B. (2006). Predicting climate-induced range shifts: Model differences and model reliability. Global Change Biology, 12, 1568–1584. https://doi.org/10.1111/j.1365-2486.2006.01191.x.
Cutler, D. R., Edwards, T. C., Beard, K. H., et al. (2007). Random forests for classification in ecology. Ecology, 88, 2783–2792. https://doi.org/10.1890/07-0539.1.
Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
Vincenzi, S., Zucchetta, M., Franzoi, P., et al. (2011). Application of a random forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy. Ecological Modelling, 222, 1471–1478. https://doi.org/10.1016/j.ecolmodel.2011.02.007.
Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406. https://doi.org/10.1016/j.jag.2012.03.012.
Fukuda, S., Spreer, W., Yasunaga, E., et al. (2013). Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agricultural Water Management, 116, 142–150. https://doi.org/10.1016/j.agwat.2012.07.003.
Jeong, J. H., Resop, J. P., Mueller, N. D., & Fleisher, D. H. (2016). Random forests for global and regional crop yield predictions. PLoS ONE. https://doi.org/10.1371/journal.pone.0156571.
Climate Maharashtra: Temperature, climate graph, Climate table for Maharashtra—Climate-Data.org. Retrieved Jun 27, 2019, from https://en.climate-data.org/asia/india/maharashtra-747/.
(2019) Directorate of Economics And Statistics, Ministry of Agriculture, GoI. Retrieved July 17, 2019, from https://eands.dacnet.nic.in/.
(2019) LP DAAC—AppEEARS. Retrieved May 13, 2019, from https://lpdaac.usgs.gov/tools/appeears/.
Kern, A., Marjanović, H., Dobor, L., Anić, M., Hlásny, T., & Barcza, Z. (2017). Identification of years with extreme vegetation State in Central Europe based on remote sensing and meteorological data. SEEFOR, 8, 1–20. https://doi.org/10.15177/seefor.17-05.
Kern, A., Marjanović, H., Barcza, Z., et al. (2016). Evaluation of the quality of NDVI3g dataset against collection 6 MODIS NDVI in Central Europe between 2000 and 2013. Remote Sensing, 8, 955. https://doi.org/10.3390/rs8110955.
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91–100.
(2012) SPI User Guide.
(2017) Package “SPEI.” https://doi.org/10.1175/2009JCLI2909.1.
ECMWF | ERA Interim, Daily. Retrieved May 1, 2019, from https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/.
Liaw, A., & Wiener, M. (2018). randomForest Title Breiman and Cutler’s random forests for classification and regression. R Package Version. https://doi.org/10.1023/A:1010933404324.
Breiman, L., Cutler, A. (2004). Random forest-manual. https://www.stat.berkeley.edu/~breiman/RandomForests/cc_manual.htm.
Guan, H., Li, J., Chapman, M., et al. (2013). Integration of orthoimagery and lidar data for object-based urban thematic map** using random forests. International Journal of Remote Sensing, 34, 5166–5186. https://doi.org/10.1080/01431161.2013.788261.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Ballesteros, R., Ortega, J. F., Hernandez, D., et al. (2018). Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring. International Journal of Applied Earth Observation and Geoinformation, 72, 66–75. https://doi.org/10.1016/j.jag.2018.05.019.
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69.
Srinet, R., Nandy, S., & Patel, N. R. (2019). Estimating leaf area index and light extinction coefficient using random forest regression algorithm in a tropical moist deciduous forest, India. Ecological Informatics, 52, 94–102. https://doi.org/10.1016/j.ecoinf.2019.05.008.
Segal, M. R. (2003). UC San Francisco Recent Work Title Machine Learning Benchmarks and Random Forest Regression Publication Date Machine Learning Benchmarks and Random Forest Regression.
Acknowledgements
This research was carried out as a part of SUFALAM project funded by ISRO. The authors are very much thankful to the Director, Indian Institute of Remote Sensing, Dehradun for his invaluable support during the research work. The authors are also very much thankful to the anonymous reviewers.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Prasad, N.R., Patel, N.R. & Danodia, A. Crop yield prediction in cotton for regional level using random forest approach. Spat. Inf. Res. 29, 195–206 (2021). https://doi.org/10.1007/s41324-020-00346-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41324-020-00346-6