Crop Yield Estimation Using Deep Learning and Satellite Imagery
Keywords:
Crop yield estimates, Satellite imagery, production of crop, Data mining, Data collection, Deep learning, Convolutional neural networks (CNNs), Visual Geometry Group (VGG)Abstract
For efficient resource management and to guarantee food security, crop yield estimates must be accurate. Deep learning techniques combined with satellite imagery have become a potent method for predicting crop yields in recent years. Deep learning algorithms can extract Data from satellites to provide spatial and temporal information that can be used to analyze crop development patterns and environmental factors. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of these methods. For a precise estimate of crop production, satellite photography offers useful information on soil characteristics, meteorological conditions, and vegetation indices. The application of deep learning with satellite imagery for crop yield estimation is discussed in general terms in this study, including data collection, pre-processing, model selection, feature extraction, yield prediction, and model validation. The recommended method creates a comprehensive agricultural yield prediction system that links raw data to projected crop yields by fusing deep learning and data mining approaches. Incorporating the Tweak Chick Swarm Optimization method for data pre-processing, the proposed model combines the Visual Geometry Group (VGG) Net classification algorithm with a discrete deep belief network. The model outperforms other models by accurately capturing the baseline data distribution, resulting in an accuracy rate of 97% for predictions.
Downloads
References
M. van der Velde et al., “In-season performance of European Union wheat forecasts during extreme impacts,” Sci Rep, vol. 8, no. 1, pp. 1–10, Oct. 2018, doi: 10.1038/s41598-018- 33688-1.
H. Jiang et al., “A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level,” Global Change Biology, vol. n/a, no. n/a, doi: 10.1111/gcb.14885.
F. Gao, M. Anderson, C. Daughtry, and D. Johnson, “Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution MultiSatellite Imagery,” Remote Sensing, vol. 10, no. 9, p. 1489, Sep. 2018, doi: 10.3390/rs10091489.
F. Kogan et al., “Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models,” International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192–203, Aug. 2013, doi: 10.1016/j.jag.2013.01.002.
J. Liu et al., “Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada,” Remote Sensing, vol. 11, no. 20, p. 2419, Jan. 2019, doi 10.3390/rs11202419.
F. Novelli, H. Spiegel, T. Sandén, and F. Vuolo, “Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation,” Agronomy, vol. 9, no. 5, p. 255, May 2019, doi 10.3390/agronomy9050255.
N. Kim, K.-J. Ha, N.-W. Park, J. Cho, S. Hong, and Y.-W. Lee, “A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015,” ISPRS International Journal of Geo-Information, vol. 8, no. 5, p. 240, May 2019, doi: 10.3390/ijgi8050240.
J. Richetti et al., “Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil,” JARS, JARSC4, vol. 12, no. 2, p. 026029, Jun. 2018, doi: 10.1117/1.JRS.12.026029.
D. M. Johnson, “An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States,” Remote Sensing of Environment, vol. 141, pp. 116–128, Feb. 2014, doi: 10.1016/j.rse.2013.10.027.
S. Khaki and L. Wang, “Crop Yield Prediction Using Deep Neural Networks,” Front. Plant Sci., vol. 10, 2019, doi: 10.3389/fpls.2019.00621.
E. Vermote, “MOD09Q1 MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006.” NASA EOSDIS Land Processes DAAC, 2015, doi: 10.5067/MODIS/MOD09Q1.006.
Z. Wan, S. Hook, and G. Hulley, “MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006.” 2015, [Online]. Available: https://doi.org/10.5067/MODIS/MOD11A1.006.
S. Running, Q. Mu, and M. Zhao, “MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006.” 2017, [Online]. Available: https://doi.org/10.5067/MODIS/MOD16A2.006.
T. Sakamoto, B. D. Wardlow, A. A. Gitelson, S. B. Verma, A. E. Suyker, and T. J. Arkebauer, “A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data,” Remote Sensing of Environment, vol. 114, no. 10, pp. 2146–2159, Oct. 2010, doi 10.1016/j.rse.2010.04.019.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sensing of Environment, 2017, doi: 10.1016/j.rse.2017.06.031.
“USDA/NASS QuickStats Ad-hoc Query Tool.” https://quickstats.nass.usda.gov/ (accessed Jan. 08, 2020).
M. Claverie et al., “The Harmonized Landsat and Sentinel-2 surface reflectance data set,” Remote Sensing of Environment, vol. 219, pp. 145–161, Dec. 2018, doi: 10.1016/j.rse.2018.09.002.
L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
C. Pelletier, G. I. Webb, and F. Petitjean, “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series,” Remote Sensing, vol. 11, no. 5, p. 523, Jan. 2019, doi: 10.3390/rs11050523.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” 2016, pp. 265–283, Accessed: Jan. 10, 2020. [Online]. Available: https://www.usenix.org/conference/osdi16/technicalsessions/presentation/abadi.
Dange, B. J. ., Mishra, P. K. ., Metre, K. V. ., Gore, S. ., Kurkute, S. L. ., Khodke, H. E. . and Gore, S. . (2023) “Grape Vision: A CNN-Based System for Yield Component Analysis of Grape Clusters ”, International Journal of Intelligent Systems and Applications in Engineering, 11(9s), pp. 239–244. Available: https://ijisae.org/index.php/IJISAE/article/view/3113.
Gore, S. ., Dutt, I. ., Dahake, R. P. ., Khodke, H. E. ., Kurkute, S. L. ., Dange, B. J. . and Gore, S. . (2023) “Innovations in Smart City Water Supply Systems ”, International Journal of Intelligent Systems and Applications in Engineering, 11(9s), pp. 277–281. Available at: https://ijisae.org/index.php/IJISAE/article/view/3118.
Tholkapiyan, M. ., Ramadass, S. ., Seetha, J. ., Ravuri, A. ., Vidyullatha, P. ., Shankar S., S. . and Gore, S. . (2023) “Examining the Impacts of Climate Variability on Agricultural Phenology: A Comprehensive Approach Integrating Geoinformatics, Satellite Agrometeorology, and Artificial Intelligence”, International Journal of Intelligent Systems and Applications in Engineering, 11(6s), pp. 592–598. Available at: https://ijisae.org/index.php/IJISAE/article/view/2891.
Rajesh Kumar Chaudhary, M. K. C. (2021). The Role of School Management Towards Staff Motivation for Effective Performance in Nepal: During the Covid-19. International Journal of New Practices in Management and Engineering, 10(01), 01–11. https://doi.org/10.17762/ijnpme.v10i01.93
Sherje, D. N. . (2021). Thermal Property Investigation in Nanolubricants via Nano- Scaled Particle Addition. International Journal of New Practices in Management and Engineering, 10(01), 12–15. https://doi.org/10.17762/ijnpme.v10i01.96
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
