The Digital Agricultural Revolution. Группа авторов
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may be further improved by changing the input parameters. Sugarcane crop is sensitive to leaf area index (LAI), and number of stalks per meter [77]. A stronger relationship exists between sugarcane yield and rainfall. Total soil available water is an important indicator of yield. Another important point, which differs yield prediction, is input parameter as average yield. In case of sugarcane, the input is given as average of plant and ratoon for the 3 years. The improvement of model was not attempted because of the nonavailability of the data on sugarcane crop-sensitive parameters, like the number of stalks per meter and total soil available water.
Figure 2.12 Final predicted yield map of paddy during 2015 (original figure).
Figure 2.13 Final predicted yield map of sugarcane during 2015 (original figure).
2.7 Conclusion
The present study conducted with an aim to test the ability of machine learning algorithm in integration with remote sensing in crop yield prediction of paddy and sugarcane crops at regional level. Crop-sensitive parameters extracted from high-resolution LANDSAT 8 OLI imageries are used as neural network model inputs. The FFBPNN models for crop yield were developed and calibrated in MATLAB environment. During training, the model perceptrons were trained with 75 of the 100 inputs up to 10,000 epochs with 1 to 10 hidden neurons. Statistical analysis revealed the reliability of the model in paddy yield estimation. However, slight under estimation of yield of the sugarcane crop indicates sensitivity of yield algorithms to crop input parameters. It was concluded that there is a high efficacy of using remote sensing images and NN models to generate accurate crop yield maps and also revealed significant superiority of neural network models over conventional methods.
References
1. Adams, R.M., Hurd, B.H., Lenhart, S., Leary, N., Effects of global climate change on agriculture: an interpretative review. Clim. Res., 11, 1, 19–30, 1998.
2. Patel, P., Agriculture drones are finally cleared for takeoff [News]. IEEE Spectr., 53, 11, 13–14, 2016.
3. Tokekar, P., Vander Hook, J., Mulla, D., Isler, V., Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Trans. Rob., 32, 6, 1498–1511, 2016.
4. Alsalam, B.H.Y., Morton, K., Campbell, D., Gonzalez, F., Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture, in: 2017 IEEE Aerospace Conference, pp. 1–12, p. IEEE, 2017.
5. Gevaert, C.M., Suomalainen, J., Tang, J., Kooistra, L., Generation of spectral temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 6, 3140–3146, 2015.
6. Gómez-Candón, D., De Castro, A.I., López-Granados, F., Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis. Agric., 15, 1, 44–56, 2014.
7. Sarle, W.S., Neural networks and statistical models, Proceedings of the Nineteenth Annual SAS Users Group International Conference, pp. 1538–1550, 1994.
8. Hsu, K.L., Gupta, H.V., Sorooshian, S., Artificial neural network modelling of the rainfall-runoff process. Water Resour. Res., 31, 10, 2517–2530, 1995.
9. Zou, J., Han, Y., So, S.S., Overview of artificial neural networks, in: Artificial Neural Networks, pp. 14–22, 2008.
10. Lent, R., Azevedo, F.A., Andrade-Moraes, C.H., Pinto, A.V., How many neurons do you have? Some dogmas of quantitative neuroscience under revision. Eur. J. Neurosci., 35, 1, 1–9, 2012.
11. Wang, J., Tsapakis, I., Zhong, C., A space–time delay neural network model for travel time prediction. Eng. Appl. Artif. Intell., 52, 145–160, 2016.
12. Demuth, H., Beale, M., Hagan, M., Neural Network Toolbox User’s Guide, The MathWorks, Inc, Natick, MA, USA, 2017.
13. Park, J., Yi, D., Ji, S., A Novel Learning Rate Schedule in Optimization for Neural Networks and It’s Convergence. Symmetry, 12, 660, 2020.
14. Mohamed, Z.E., Using the artificial neural networks for prediction and validating solar radiation. J. Egypt. Math. Soc., 27, 1, 1–13, 2019.
15. Garg, B., Kirar, N., Menon, S., Sah, T., A performance comparison of different back propagation neural networks methods for forecasting wheat production. CSI Trans. ICT, 4, 2-4, 305–311, 2016.
16. Maladkar, K., 6 Types of Artificial Neural Networks Currently Being Used in Machine Learning, analyticsindia, 2019. [Online]. Available: https://www.analyticsindiamag.com/..
17. Park, S., Im, J., Park, S., Yoo, C., Han, H., Rhee, J., Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sens., 10, 3, 447, 2018.
18. Zhang, M., Lin, H., Wang, G., Sun, H., Fu, J., Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sens., 10, 11, 1840, 2018.
19. Wang, M., Wang, J., Chen, L., Mapping Paddy Rice Using Weakly Supervised Long Short-Term Memory Network with Time Series Sentinel Optical and SAR Images. Agriculture, 10, 10, 483, 2020.
20. Wu, Y. and Xu, L., Crop Organ Segmentation and Disease Identification Based on Weakly Supervised Deep Neural Network. Agronomy, 9, 11, 737, 2019.
21. Pourdarbani, R., Sabzi, S., García-Amicis, V.M., García-Mateos, G., Molina-Martínez, J.M., Ruiz-Canales, A., Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9, 11, 672, 2019.
22. Li, Y. and Chao, X., ANN-Based Continual Classification in Agriculture. Agriculture, 10, 5, 178, 2020.
23. Zhang, M., Lin, H., Wang, G., Sun, H., Fu, J., Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sens., 10, 11, 1840, 2018.
24. Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A., Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett., 14, 5, 778–782, 2017.
25. Sun, Z., Di, L., Fang, H., Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series. Int. J. Remote Sens., 40, 2, 593–614, 2019.
26. Garg, B., Kirar, N., Menon, S., Sah, T., A performance comparison of different back propagation neural networks methods for forecasting wheat production. CSI Trans. ICT, 4, 2-4, 305–311, 2016.
27. Ghazi, M.M., Yanikoglu, B., Aptoula, E., Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 235, 228–235, 2017.
28. Xie, C., Wang, R., Zhang, J., Chen, P., Dong, W., Li, R., Chen, H., Multilevel learning features for automatic classification of field crop pests. Comput. Electron. Agric., 152, 233–241, 2018.
29. Koirala, A., Walsh, K.B., Wang, Z.,