Teaching and Learning based Optimization with Deep Learning Model for Rice Crop Yield Prediction

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 4

Year of Publication : 2023

Authors : S. Thirumal, R. Latha

10.14445/23488379/IJEEE-V10I4P110

How to Cite?

S. Thirumal, R. Latha, "Teaching and Learning based Optimization with Deep Learning Model for Rice Crop Yield Prediction," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 4, pp. 105-114, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I4P110

Abstract:

Rice crop yield prediction suggests the procedure of estimating the rice quantity which is harvested in a provided land region dependent upon several features like farming practices, weather conditions, and soil quality. The main aim of rice crop yield prediction is to offer farmers and agricultural planners correct crop yield calculations in progress, creating informed decisions assuming harvesting, marketing, and planting their crops. It supports farmers in optimizing their production and enhancing their profitability, but also improving food security by ensuring an even supply of rice for consumers. Deep learning (DL) approaches are utilized for predicting crop yield by leveraging the influence of neural networks for learning complex patterns and connections in data. This study presents a Teaching and Learning Based Optimization with Deep Learning for Rice Crop Yield Prediction (TLBODL-RCYP) technique. The proposed TLBODL-RCYP approach emphasizes the accurate forecasting of the rice yield using DL and hyperparameter optimizers. To accomplish this, the TLBODL-RCYP technique performs different preprocessing stages to improve the data quality. Besides, the TLBODL-RCYP technique employs a hybrid Convolution Recurrent HopField Neural Network (HCRHNN) model for yield prediction. At last, the TLBO algorithm was utilized to adjust the hyperparameter values of the HCRHNN technique and thereby enhance the predictive results. The experimental outcome investigation of the TLBODL-RCYP approach is tested with the Kaggle dataset, and the outcomes assured the improvized predictive results of the TLBODL-RCYP method over other recent DL techniques.

Keywords:

Agriculture, Rice crop yield, Prediction models, Deep learning, Metaheuristics.

References:

[1] Martin Kuradusenge et al., “Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize,” Agriculture, vol. 13, no. 1, p. 225, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[2] Farhat Abbas et al., “Crop yield Prediction through Proximal Sensing and Machine Learning Algorithms,” Agronomy, vol. 10, no. 7, p. 1046, 2020.
[CrossRef] [Google Scholar] [Publisher link]
[3] Xinbin Zhou et al., “Predicting Within-field Variability in Grain Yield and Protein Content of Winter Wheat using UAV-based Multispectral Imagery and Machine Learning Approaches,” Plant Production Science, vol. 24, no. 2, pp.137-151, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[4] V.V. Narendra Kumar, and T. Satish Kumar, “Smarter Artificial Intelligence with Deep Learning,” SSRG International Journal of Computer Science and Engineering, vol. 5, no. 6, pp. 10-16, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[5] A. Chandra et al., “Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-temporal Vegetation Indices and Weather and Non-weather Variables,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.187-194, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[6] Nguyen-Thanh Son et al., “Field-scale Rice Yield Prediction from Sentinel-2 Monthly Image Composites using Machine Learning Algorithms,” Ecological Informatics, vol. 69, p. 101618, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[7] M. Shanmugam Shobha et al., “Crop Yield Production using Machine Learning Algorithm,” International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 4, pp.3328-3330, 2020.
[Google Scholar]
[8] Noraini Azmi et al., “RF-based Moisture Content Determination in Rice Using Machine Learning Techniques,” Sensors, vol. 21, no. 5, p. 1875, 2021. [CrossRef] [CrossRef] [Google Scholar] [Publisher link]
[9] D. Sheema et al., “The Detection and Identification of Pest-FAW Infestation in Maize Crops Using Iot-Based Deep-Learning Algorithm,” SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 12, pp. 180-188, 2022.
[CrossRef] [Publisher link]
[10] G.P. Dimf, P. Kumar, and K. Paul Joshua, “CNN with BI-LSTM Electricity Theft Detection based on Modified Cheetah Optimization Algorithm in Deep Learning,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 2, pp. 35-43, 2023.
[CrossRef] [Publisher link]
[11] Joe Vicente Herrera, and Victor Romero-Alva, “Technified Irrigation System for Crops using Automated Technology to Optimize Water Consumption in an Efficient Way,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 52-58, 2023.
[CrossRef] [Publisher link]
[12] K. Vignesh, A. Askarunisa, and A.M. Abirami, “Optimized Deep Learning Methods for Crop Yield Prediction,” Computer Systems Science and Engineering, vol. 44, no. 2, pp.1051-1067, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[13] Bifta Sama Bari et al., “A Real-time Approach of Diagnosing Rice Leaf Disease using Deep Learning-based Faster R-CNN Framework,” Peer J Computer Science, vol. 7, p. e432, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[14] Heri Andrianto et al., “Smartphone Application for Deep Learning-based Rice Plant Disease Detection,” 2020 International Conference on Information Technology Systems and Innovation (ICITSI), 2020.
[CrossRef] [Google Scholar] [Publisher link]
[15] Melese Zekiwos, and Abey Bruck, “Deep Learning-based Image Processing for Cotton Leaf Disease and Pest Diagnosis,” Journal of Electrical and Computer Engineering, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[16] R. Sujatha et al., “Performance of deep Learning VS Machine Learning in Plant Leaf Disease Detection,” Microprocessors and Microsystems, vol. 80, p. 103615, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[17] Juan Cao et al., “Integrating Multi-source Data for Rice Yield Prediction Across China using Machine Learning and Deep Learning Approaches,” Agricultural and Forest Meteorology, vol. 297, p. 108275, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[18] Devdatta A. Bondre, and Santhosh Mahagaonkar, “Prediction of Crop Yield and Fertilizer Recommendation using Machine Learning Algorithms,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 5, pp. 371-376, 2019.
[Google Scholar]
[19] Saeed Nosratabadi et al., “Prediction of Food Production using Machine Learning Algorithms of Multilayer Perceptron and ANFIS,” Agriculture, vol. 11, no. 5, p. 408, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[20] Adarsh Ravi Mishra, Ravi Mishra, and Ragini Shukla, “A Cloud-Centric Real-Time Telemonitoring System for Cardiac Patients based on the Internet of Medical Things,” International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 105-119, 2023.
[CrossRef] [Publisher link]
[21] Ramesh A. Medar, and Vijay S. Rajpurohit, and Anand M. Ambekar, “Sugarcane Crop Yield Forecasting Model using Supervised Machine Learning,” International Journal of Intelligent Systems and Applications, vol. 11, no. 8, pp. 11-20, 2019.
[CrossRef] [Google Scholar]
[22] Dilli Paudel et al., “Machine Learning for Regional Crop Yield Forecasting in Europe,” Field Crops Research, vol. 276, p. 108377, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[23] David F. Nettleton et al., “Predicting Rice Blast Disease: Machine Learning Versus Process-based Models,” BMC Bioinformatics, vol. 20, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[24] Raúl Jáuregui-Velarde et al., “Expert System for the Timely Diagnosis of Infectious Diseases,” International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 215-226, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[25] Shweta Lamba et al., “A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning,” Sustainability, vol. 15, no. 2, p. 1502, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[26] Vikas Kamra, Praveen Kumar, and Masoud Mohammadian, “An Intelligent Disease Prediction System for Psychological Diseases by Implementing Hybrid Hopfield Recurrent Neural Network Approach,” Intelligent Systems with Applications, vol. 18, p. 200208, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[27] Raji Krishna, and S. Hemamalini, “Optimal Energy Management of Virtual Power Plants with Storage Devices Using Teaching-and-Learning-Based Optimization Algorithm,” International Transactions on Electrical Energy Systems, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[28] Vinushi Amaratunga et al., “Artificial Neural Network to Estimate the Paddy Yield Prediction using Climatic Data,” Mathematical Problems in Engineering, 2020.
[CrossRef] [Google Scholar] [Publisher link]