ABSTRACT:
The important aspects of agriculture are drought prediction and crop yield prediction. Farmers and policymakers can make decisions with the help of accurate and timely prediction of crop yield and drought. The most used statistical models are tedious and time consuming. Nowadays, deep learning techniques attained more attention in smart farming field due to their outstanding performance. The precise predictions can be easily prepared by Deep Learning (DL) techniques with the features extracted from big data. In this paper, the DL based crop yield and the drought prediction model are developed with the combination of Deep Belied Network (DBN), convolutional layer and attention mechanism called the CAW-DBN scheme. Moreover, novel feature extraction and feature selection approaches are proposed to decrease the computational complexity and enhance the performance of the proposed model. The performance of the proposed scheme is evaluated with three agricultural datasets and compared with existing DL methods such as DNN, CNN, LSTM, and Bi-LSTM. The experimental results show that the suggested approach can accurately predict the outcomes with exceptional performance than the existing approaches with accuracy above 90% for all the datasets.
Cite this article:
Nikhil Gondaliya. Crop Yield and Drought Prediction Using CAW-DBN Classifier. Research Journal of Engineering and Technology. 2026;17(2):77-0. doi: 10.52711/2321-581X.2026.00012
Cite(Electronic):
Nikhil Gondaliya. Crop Yield and Drought Prediction Using CAW-DBN Classifier. Research Journal of Engineering and Technology. 2026;17(2):77-0. doi: 10.52711/2321-581X.2026.00012 Available on: https://rjetonline.com/AbstractView.aspx?PID=2026-17-2-5
6. REFERENCES:
1. Kaur, A., and Sood, S. K. Cloud-centric IoT-based green framework for smart drought prediction. IEEE Internet of Things Journal. 2019; 7(2): 1111-1121.
2. Rezk, N. G., Hemdan, E. E. D., Attia, A. F., El-Sayed, A., and El-Rashidy, M. A. An efficient IoT based smart farming system using machine learning algorithms. Multimedia Tools and Applications. 2021; 80(1): 773-797.
3. Prodhan, F. A., Zhang, J., Sharma, T. P. P., Nanzad, L., Zhang, D., Seka, A. M., and Mohana, H. P. Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach. Science of The Total Environment. 2022; 807: 151029.
4. Balducci, F., Impedovo, D., and Pirlo, G. Machine learning applications on agricultural datasets for smart farm enhancement. Machines. 2018; 6(3): 38.
5. Ünal, Z. Smart farming becomes even smarter with deep learning—a bibliographical analysis. IEEE Access. 2020; 8: 105587-105609.
6. Sishodia, R. P., Ray, R. L., and Singh, S. K. Applications of remote sensing in precision agriculture: A review. Remote Sensing. 2020; 12(19): 3136.
7. Goel, R. K., Yadav, C. S., Vishnoi, S., and Rastogi, R. Smart agriculture–Urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems. 2021; 30: 100512.
8. Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., .and Goudos, S. K. Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things. 2022; 18: 100187.
9. Islam, S., Jamwal, S., and Mir, M. H. Leveraging Fog Computing for Smart Internet of Things Crop Monitoring Farming in Covid-19 Era. Annals of the Romanian Society for Cell Biology. 2021; 25(6): 10410-10420.
10. Li, C., andNiu, B. Design of smart agriculture based on big data and Internet of things. International Journal of Distributed Sensor Networks. 2020; 16(5): 1550147720917065.
11. Kaur, A., and Sood, S. K. Deep learning based drought assessment and prediction framework. Ecological Informatics. 2020; 57: 101067.
12. Kaur, A., and Sood, S. K. Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm. Journal of Experimental and Theoretical Artificial Intelligence. 2020; 32(2): 273-289.
13. Nevavuori, P., Narra, N., and Lipping, T. Crop yield prediction with deep convolutional neural networks. Computers and electronics in agriculture2019), 163, 104859.
14. Elavarasan, D., and Vincent, P. D. Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access. 2020; 8: 86886-86901.
15. Agana, N. A., andHomaifar, A. (2017, March). A deep learning based approach for long-term drought prediction. In SoutheastCon 2017 (pp. 1-8).
16. Balakrishnan, N., and Muthukumarasamy, G. Crop production-ensemble machine learning model for prediction. International Journal of Computer Science and Software Engineering. 2016. 5(7): 148.
17. Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., andWanichsombat, A. (2019). IoT and agriculture data analysis for smart farm Computers and Electronics in Agriculture.
18. Mahbub, M. A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network. Internet of Things. 2020; 9: 100161.
19. Demisse, G. B., Tadesse, T., Atnafu, S., Hill, S., Wardlow, B. D., Bayissa, Y., andShiferaw, A. Information mining from heterogeneous data sources: a case study on drought predictions. Information. 2017; 8(3): 79.
20. Marndi, A., Ramesh, K. V., and Patra, G. K. Crop production estimation using deep learning technique. Current Science. 2021; 121(8): 1073.
21. Talasila, V., Prasad, C., Reddy, G. T. S., and Aparna, A. Analysis and prediction of crop production in Andhra region using deep convolutional regression network. Int. J. Intell. Eng. Syst. 2020; 13(5): 1-9.
22. Bazrafshan, O., Ehteram, M., Latif, S. D., Huang, Y. F., Teo, F. Y., Ahmed, A. N., and El-Shafie, A. Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models. Ain Shams Engineering Journal. 2022; 13(5): 101724.
23. Gavahi, K., Abbaszadeh, P., and Moradkhani, H. Deep Yield: A combined convolutional neural network with long short-term memory for crop yield forecasting. Expert Systems with Applications. 2021; 184: 115511.
24. Elavarasan, D., Vincent PM, D. R., Srinivasan, K., and Chang, C. Y. A hybrid CFS filter and RF-RFE wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling. Agriculture. 2020; 10(9): 400.
25. Tripathi, A., Tiwari, R. K., and Tiwari, S. P. A deep learning multi-layer perceptron and remote sensing approach for soil health based crop yield estimation. International Journal of Applied Earth Observation and Geoinformation. 2022; 113: 102959.
26. Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N. H., and Islam, N. A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sensing. 2022; 14(9): 1990.
27. Khaki, S., Wang, L., and Archontoulis, S. V. A cnn-rnn framework for crop yield prediction. Frontiers in Plant Science2020), 10, 1750.
28. Elavarasan, D., and Vincent, P. M. A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. Journal of Ambient Intelligence and Humanized Computing2021, 12(11), 10009-10022.
29. Jin, X., Zhang, J., Kong, J., Su, T., and Bai, Y. A reversible automatic selection normalization (RASN) deep network for predicting in the smart agriculture system. Agronomy. 2022; 12(3): 591.
30. Tseng, F. H., Cho, H. H., and Wu, H. T. Applying big data for intelligent agriculture-based crop selection analysis. IEEE Access. 2019; 7: 116965-116974.
31. Abba, S. I., Pham, Q. B., Usman, A. G., Linh, N. T. T., Aliyu, D. S., Nguyen, Q., and Bach, Q. V. Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant. Journal of Water Process Engineering. 2020; 33: 101081.
32. Hekmatmanesh, A., Wu, H., Jamaloo, F., Li, M., and Handroos, H. A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications. Multimedia Tools and Applications. 2020; 79(25): 17521-17549.
33. Liu, X., Li, Z., Zhang, Z., and Zhang, G. Coal and gas outbursts prediction based on combination of hybrid feature extraction DWT+ FICA–LDA and optimized QPSO-DELM classifier. The Journal of Supercomputing. 2022; 78(2): 2909-2936.
34. Tran, M. Q., Elsisi, M., and Liu, M. K. Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis. Measurement. 2021; 184: 109962.
35. da Costa, N. L., de Lima, M. D., and Barbosa, R. Evaluation of feature selection methods based on artificial neural network weights. Expert Systems with Applications. 2021; 168: 114312.
36. Khan, M. A. HCRNNIDS: hybrid convolutional recurrent neural network-based network intrusion detection system. Processes. 2021; 9(5): 834.
37. Li, Y., Xu, H., Bian, M., and Xiao, J. Attention based CNN-ConvLSTM for pedestrian attribute recognition. Sensors. 2020; 20(3): 811.
38. Manimurugan, S., Al-Mutairi, S., Aborokbah, M. M., Chilamkurti, N., Ganesan, S., andPatan, R. Effective attack detection in internet of medical things smart environment using a deep belief neural network. IEEE Access. 2020; 8: 77396-77404.
39. Javeed, M., Gochoo, M., Jalal, A., and Kim, K. HF-SPHR: Hybrid features for sustainable physical healthcare pattern recognition using deep belief networks. Sustainability. 2021; 13(4): 1699.
40. Mohan, P., and Patil, K. K. Deep learning based weighted SOM to forecast weather and crop prediction for agriculture application. Int. J. Intell. Eng. Syst. 2018; 11: 167-176.
41. Anguraj, D. K., Mandhala, V. N., Bhattacharyya, D., and Kim, T. H. Hybrid neural network classification for irrigation control in WSN based precision agriculture. Journal of Ambient Intelligence and Humanized Computing. 2021: 1-12.
42. Van Houdt, G., Mosquera, C., andNápoles, G. A review on the long short-term memory model. Artificial Intelligence Review. 2020; 53(8): 5929-5955.
43. Petroșanu, D. M., and Pîrjan, A. Electricity consumption forecasting based on a bidirectional long-short-term memory artificial neural network. Sustainability. 2020; 13(1): 104.