Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region
Received: 21 September 2024 | Revised: 15 October 2024, 4 December 2024 and 10 December 2024 | Accepted: 19 December 2024 | Online: 24 December 2024
Corresponding author: S. Vimalkumar
Abstract
Soil moisture is a critical determinant of the maize crop health and productivity. With over 60% of India's maize cultivation concentrated in South Indian states, accurately forecasting soil moisture is essential for optimizing irrigation and enhancing agricultural output. This study introduces an Improved Hybrid Machine Learning (IHML) model that integrates and optimizes Machine Learning (ML) models to deliver superior predictive performance. By leveraging data from key maize-growing districts in South India, the IHML model demonstrates enhanced convergence rates and accuracy compared to traditional ML approaches. The research framework is grounded in comprehensive correlation evaluations, which inform parameter selection and model architecture. Extensive comparisons reveal that the IHML model significantly outperforms individual ML models in forecasting soil moisture with higher precision. These findings highlight the potential of IHML models to advance smart farming practices and enable precise irrigation management, paving the way for improved crop yield and sustainable agriculture.
Keywords:
ensemble methods, error margin, moisture content, prediction, maize cultivation, temperature, rainfall, precipitationDownloads
References
M. K. Gill, T. Asefa, M. W. Kemblowski, and M. McKee, "Soil Moisture Prediction Using Support Vector Machines," JAWRA Journal of the American Water Resources Association, vol. 42, no. 4, pp. 1033–1046, 2006.
"Crops and livestock products" FAOSTAT.
E. C. Martin, "Methods of Determining When to Irrigate," The Univeristy of Arizona Coopertive Extension.
I. M. K. Artoshi, L. A. Abdulateef, I. H. Farman, and A. M. Ahmed, "Efficiency and Durability Assessment of Soil Stabilization using Waste Tire Shreds," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 13012–13016, Feb. 2024.
A. Mabrouk, M. Jamei, and A. Ahmed, "Measured and Predicted Unsaturated Permeability of Cracked Compacted Fine Soil," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 13953–13958, Jun. 2024.
C. A. Scott, W. G. M. Bastiaanssen, and M.-D. Ahmad, "Mapping Root Zone Soil Moisture Using Remotely Sensed Optical Imagery," Journal of Irrigation and Drainage Engineering, vol. 129, no. 5, pp. 326–335, Oct. 2003.
C. Notarnicola, M. Angiulli, and F. Posa, "Soil moisture retrieval from remotely sensed data: Neural network approach versus Bayesian method," IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 2, pp. 547–557, Oct. 2008.
A. Pandey, S. K. Jha, J. K. Srivastava, and R. Prasad, "Artificial neural network for the estimation of soil moisture and surface roughness," Russian Agricultural Sciences, vol. 36, no. 6, pp. 428–432, Dec. 2010.
Y. L. Then, K. Y. You, M. N. Dimon, and C. Y. Lee, "A modified microstrip ring resonator sensor with lumped element modeling for soil moisture and dielectric predictions measurement," Measurement, vol. 94, pp. 119–125, Dec. 2016.
J. E. Holland and A. Biswas, "Predicting the mobile water content of vineyard soils in New South Wales, Australia," Agricultural Water Management, vol. 148, pp. 34–42, Jan. 2015.
P. V. D. de Souza, L. P. de Rezende, A. P. Duarte, and G. V. Miranda, "Maize Yield Prediction using Artificial Neural Networks based on a Trial Network Dataset," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10338–10346, Apr. 2023.
Salem-District Diagnostic Report (DDR), 2019.
Dindigul-District Diagnostic Report (DDR),2019.
Namakkal-District Diagnostic Report (DDR),2019.
"India Dataset," SWAT | Soil & Water Assessment Tool.
S. Pandiyan, M. Ashwin, R. Manikandan, K. K. M. Raghunath, and A. G. R. Raman, "Heterogeneous Internet of things organization Predictive Analysis Platform for Apple Leaf Diseases Recognition," Computer Communications, vol. 154, pp. 99–110, Mar. 2020.
K. Fang and C. Shen, "Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel," Journal of Hydrometeorology, vol. 21, no. 3, pp. 399–413, 2020.
C. Xiong, S. Merity, and R. Socher, "Dynamic Memory Networks for Visual and Textual Question Answering," in Proceedings of The 33rd International Conference on Machine Learning, Jun. 2016, pp. 2397–2406.
C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sep. 1995.
H. Drucker, D. Wu, and V. N. Vapnik, "Support vector machines for spam categorization," IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1048–1054, Sep. 1999.
M. Sabzevari, G. Martínez-Muñoz, and A. Suárez, "Building heterogeneous ensembles by pooling homogeneous ensembles," International Journal of Machine Learning and Cybernetics, vol. 13, no. 2, pp. 551–558, Feb. 2022.
U. Acharya, A. L. M. Daigh, and P. G. Oduor, "Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North," Soil Systems, vol. 5, no. 4, Dec. 2021, Art. no. 57.
Downloads
How to Cite
License
Copyright (c) 2024 S. Vimalkumar, R. Latha
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
