A novel approach for predicting net irrigated area in India using hybrid deep learning architectures

N V Palanichamy; M Kalpana; N Balakrishnan; A Suresh; V  Balamurugan; M  Rajavel; R  Dhivya; M Santhosh Kumar

doi:10.14719/pst.7412

Authors

Narasimma Venkatesa Palanichamy Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-1273-7347
M Kalpana Office of the Dean (Agriculture), Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-0201-6968
N Balakrishnan Office of the Dean (Agriculture), Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0001-6461-7374
A Suresh Office of the Dean (Agriculture), Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0005-0999-6638
V Balamurugan Office of the Dean (Agriculture), Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0002-5236-9382
M Rajavel Office of the Public Relations, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0000-0001-7133-7274
R Dhivya Department of Business Administration, PSGR Krishnammal College for Women, Coimbatore 641 004, Tamil Nadu, India https://orcid.org/0000-0001-7121-6173
M Santhosh Kumar Department of Agricultural and Rural Management, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India https://orcid.org/0009-0006-4618-3297

DOI:

https://doi.org/10.14719/pst.7412

Keywords:

deep learning, India, irrigation, prediction, water resources

Abstract

Studying irrigation systems is crucial to ensuring efficient freshwater utilization and conservation. This study examines the efficacy of forecasting the net irrigated area for future generations to create a model of prediction that can efficiently exchange water demand. To improve the forecast, we generate a model using two-hybrid deep learning techniques to predict irrigation demands: Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). These models effectively capture complex variables from diverse data sources, including rainfall patterns, irrigated area statistics and various irrigation system parameters. The main ideas, noteworthy contributions and crucial quantitative results from our study on net irrigated area projection are outlined in this publication. Our main contribution is the development of unique hybrid deep learning approaches that effectively integrate the CNN-LSTM and CNN-GRU architectures. Better predictions are made possible by the models’ design, which consists of parallel CNN layers that independently interpret certain input features. Thorough examinations of these situations validated the models’ effectiveness and led to notable decreases in important evaluation parameters, such as the RMSE, MSE, MAE and R2. Regarding excellent accuracy in predicting and overall performance, our CNN-GRU hybrid deep learning model outperformed the other models in the present research.

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References

Raei E, Asanjan AA, Nikoo MR, Sadegh M, Pourshahabi S, Adamowski JF. A deep learning image segmentation model for agricultural irrigation system classification. Comp Elect Agric. 2022;1;198:106977. https://doi.org/10.3390/agronomy14030432

Oumarou Abdoulaye A, Lu H, Zhu Y, Alhaj Hamoud Y, Sheteiwy M. The global trend of the net irrigation water requirement of maize from 1960 to 2050. Climate. 2019;7(10):124. https://doi.org/10.3390/cli7100124

Ray S, Bhattacharyya B. Availability in different source of irrigation in India: a statistical approach. Ecosystem. 2015;109.

Puy A, Lo Piano S, Saltelli A. Current models underestimate future irrigated areas. Geophys Res Lett. 2020;28;47(8):e2020GL087360. https://doi.org/10.1029/2020GL087360

Government of India, Ministry of Finance. Economic Survey 2022-23 [Internet]. New Delhi: Department of Economic Affairs, Economic Division; 2023 [cited 2025 Jan 30]. https://www.indiabudget.gov.in/economicsurvey/

Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: A survey. Computers and electronics in agriculture. 2018; 1;147:70-90. https://doi.org/10.1016/j.compag.2018.02.016

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012;25.

Sermanet P. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229. 2013.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 28;521(7553):436–44. https://doi.org/10.1038/nature14539

Kumar A, Islam T, Sekimoto Y, Mattmann C, Wilson B. Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data. Plos One. 2020; 11;15(3):e0230114. https://doi.org/10.1371/journal.pone.0230114

Devyatkin D, Otmakhova Y. Methods for mid-term forecasting of crop export and production. Applied Sci. 2021;11(22):10973. https://doi.org/10.3390/app112210973

You J, Li X, Low M, Lobell D, Ermon S. DeepGaussian process for crop yield prediction based on remote sensing data. In: Proceedings of the AAAI conference on artificial intelligence 2017;31. https://doi.org/10.1609/aaai.v31i1.11172

Alhnaity B, Pearson S, Leontidis G, Kollias S. Using deep learning to predict plant growth and yield in greenhouse environments. In: International Symposium on Advanced Technologies and Management for Innovative Greenhouses: GreenSys 2019;6:425–32. https://doi.org/10.17660/ActaHortic.2020.1296.55

Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances Neural Info Proces Sys. 2015;28.

Li P, Zhang J, Krebs P. Prediction of flow based on a CNN-LSTM combined deep learning approach. Water. 2022;21;14(6):993. https://doi.org/10.3390/w14060993

Karasu S, Altan A. Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy. 2022;242:122964. https://doi.org/10.1016/j.energy.2021.122964

Yu J, Zhang X, Xu L, Dong J, Zhangzhong L. A hybrid CNN-GRU model for predicting soil moisture in maize root zone. Agric Water Manage. 2021;28;245:106649. https://doi.org/10.1016/j.agwat.2020.106649

Chen L, Yan H, Yan J, Wang J, Tao T, Xin K, Li S, Pu Z, Qiu J. Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution. J Hydrol. 2022; 1;606:127440. https://doi.org/10.1016/j.jhydrol.2022.127440

Wang J, Wang P, Tian H, Tansey K, Liu J, Quan W. A deep learning framework combining CNN and GRU for improving wheat yield estimates using time series remotely sensed multi-variables. Comp Elect Agric. 2023;206:107705. https://doi.org/10.1016/j.compag.2023.107705

Gao G, Wang M, Huang H, Tang W. Agricultural Irrigation Area Prediction based on improved random forest model. Research Square. 2021. https://doi.org/10.21203/rs.3.rs-156767/v1

Indiastat. Indiastat [Internet]. 2024 [cited 2025 Jan 30]. https://www.indiastat.com/

Government of India, Ministry of Jal Shakti. Ministry of Jal Shakti [Internet]. 2024 [cited 2025 Jan 30]. https://jalshakti-dowr.gov.in/

Wang K, Qi X, Liu H. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Applied Ener. 2019;1;251:113315. https://doi.org/10.1016/j.apenergy.2019.113315

Hwang HP, Ku CC, Chan JC. Detection of malfunctioning photovoltaic modules based on machine learning algorithms. IEEE Access. 2021;2;9:37210–9. https://doi.org/10.1109/ACCESS.2021.3063461

Memarzadeh G, Keynia F. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Conv Manage. 2020; 1;213:112824. https://doi.org/10.1016/j.enconman.2020.112824

Shao X, Kim CS, Kim DG. Accurate multi-scale feature fusion CNN for time series classification in smart factory. Comput Mater Contin. 2020;65(1):543–61. https://doi.org/10.32604/cmc.2020.011108

He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceed IEEE International Conf Comp Vision 2015;1026–34. https://doi.org/10.1109/ICCV.2015.123

Hochreiter S. Long short-term memory. Neural Computation MIT-Press. 1997. https://doi.org/10.1162/neco.1997.9.8.1735

Li P, Zhang J, Krebs P. Prediction of flow based on a CNN-LSTM combined deep learning approach. Water. 2022;14(6), 993. https://doi.org/10.3390/w14060993

Saunders A, Drew DM, Brink W. Machine learning models perform better than traditional empirical models for stomatal conductance when applied to multiple tree species across different forest biomes. Trees For Peop. 2021;6:100139. https://doi.org/10.1016/j.tfp.2021.100139

Ji L, Fu C, Ju Z, Shi Y, Wu S, Tao L. Short-Term canyon wind speed prediction based on CNN-GRU transfer learning. Atmosphere. 2022;16;13(5):813. https://doi.org/10.3390/atmos13050813

Zhao X, Wei H, Wang H, Zhu T, Zhang K. 3D-CNN-based feature extraction of ground-based cloud images for direct normal irradiance prediction. Solar Energy. 2019; 15;181:510–8. https://doi.org/10.1016/j.solener.2019.01.096

Ullah W, Ullah A, Hussain T, Khan ZA, Baik SW. An efficient anomaly recognition framework using an attention residual LSTM in surveillance videos. Sensors. 2021; 16;21(8):2811. https://doi.org/10.3390/s21082811

Ullah W, Hussain T, Khan ZA, Haroon U, Baik SW. Intelligent dual stream CNN and echo state network for anomaly detection. Knowledge-Based Systems. 2022; 11;253:109456. https://doi.org/10.1016/j.knosys.2022.109456

Lu W, Li J, Li Y, Sun A, Wang J. A CNN?LSTM?based model to forecast stock prices. Complexity. 2020;(1):6622927. https://doi.org/10.1155/2020/6622927

Pan D, Zhang Y, Deng Y, Van Griensven Thé J, Yang SX, Gharabaghi B. dissolved oxygen forecasting for lake eries’ central basin using hybrid long short-term memory and gated recurrent unit networks. Water. 2024;28;16(5):707. https://doi.org/10.3390/w16050707

Saeed A, Alsini A, Amin D. Water quality multivariate forecasting using deep learning in a West Australian estuary. Environ Model Soft. 2024;171:105884. https://doi.org/10.1016/j.envsoft.2023.105884

Hu Y, Liu C, Wollheim WM. Prediction of riverine daily minimum dissolved oxygen concentrations using hybrid deep learning and routine hydrometeorological data. Sci Tot Environ. 2024; 25;918:170383. https://doi.org/10.1016/j.scitotenv.2024.170383

Song H, Choi H. Forecasting stock market indices using the recurrent neural network based hybrid models: CNN-LSTM, GRU-CNN and ensemble models. App Sci. 2023; 6;13(7):4644. https://doi.org/10.3390/app13074644

Wu L, Kong C, Hao X, Chen W. A short?term load forecasting method based on GRU?CNN hybrid neural network model. Math Prob Engineer. 2020;(1):1428104. https://doi.org/10.1155/2020/1428104

Faseeh M, Khan MA, Iqbal N, Qayyum F, Mehmood A, Kim J. Enhancing user experience on q&a platforms: measuring text similarity based on hybrid cnn-lstm model for efficient duplicate question detection. IEEE Access. 2024;25. https://doi.org/10.1109/ACCESS.2024.3358422

Sabri M, El Hassouni M. A novel deep learning approach for short term photovoltaic power forecasting based on GRU-CNN model. EDP Sciences E3S. 2022;336:00064).. https://doi.org/10.1051/e3sconf/202233600064

Jafari S, Byun YC. A CNN-GRU Approach to the accurate prediction of batteries' remaining useful life from charging profiles. Computers. 2023;27;12(11):219. https://doi.org/10.3390/computers12110219

Zheng W, Zheng K, Gao L, Zhangzhong L, Lan R, Xu L, Yu J. GRU-Transformer: a novel hybrid model for predicting soil moisture content in root zones. Agron. 2024;23;14(3):432. https://doi.org/10.3390/agronomy14030432

Chaudhuri S, Roy M, McDonald LM, Emendack Y. Land degradation–desertification in relation to farming practices in India: An overview of current practices and agro-policy perspectives. Sustainability. 2023;7;15(8):6383. https://doi.org/10.3390/15086383

Umutoni L, Samadi V. Application of machine learning approaches in supporting irrigation decision making: A review. Agric Wat Manage. 2024;294:108710. https://doi.org/10.1016/j.agwat.2024.108710

Dolaptsis K, Pantazi XE, Paraskevas C, Arslan S, Tekin Y, Bantchina BB, Ulusoy Y, Gündo?du KS, Qaswar M, Bustan D, Mouazen AM. A hybrid lstm approach for irrigation scheduling in maize crop. Agriculture. 2024;28;14(2):210. https://doi.org/10.3390/agriculture14020210

Mateus BC, Mendes M, Farinha JT, Assis R, Cardoso AM. Comparing LSTM and GRU models to predict the condition of a pulp paper press. Energies. 2021;22;14(21):6958. https://doi.org/10.3390/en14216958

Widiasari IR, Efendi R. Utilizing LSTM-GRU for IOT-based water level prediction using multi-variable rainfall time series data. Informs. 2024;11(4):73. https://doi.org/10.3390/informatics11040073