Enhanced Prediction of Jasmine 105 Rice Growth with RC-ELM and Slow-Release Organic Fertilizers
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Abstract
This study explores the use of a Residual Compensation Extreme Learning Machine (RC-ELM) to predict the growth of Jasmine 105 rice, specifically in the context of slow-release organic fertilizers (SROFs). The experiment involved four types of fertilizers: Cow Manure, Filter Cake, Aerated Compost, and a standard chemical control (27-12-6). The macronutrient content of each fertilizer was used as key input variables in the RC-ELM model, with real-time field sensor data providing insights. After extensive preprocessing through normalization and feature engineering, RC-ELM demonstrated superior performance compared to traditional models, such as Linear Regression, Support Vector Machines (SVM), and standard ELM variants. In particular, RC-ELM achieved an R2 = 0.9609, Y=14.982x 103.58 for Aerated Compost, reducing the Mean Squared Error (MSE) by 30%. The results indicate that while organic fertilizers like Aerated Compost may incur higher costs, they offer long-term sustainability benefits, including improved soil fertility. The study further highlights the importance of adopting organic agricultural practices, which align with internationally recognized standards, such as Organic Thailand and IFOAM, for food safety and environmental preservation. These findings underscore the potential of RC-ELM in enhancing crop yield predictions while supporting sustainable farming practices.
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References
A. Vanavichit, W. Kamolsukyeunyong, M. Siangliw, J. L. Siangliw, S. Traprab, S. Ruengphayak and S. Tragoonrung, “Thai Hom Mali Rice: origin and breeding for subsistence rainfed lowland rice system,” Rice, vol. 11, pp. 1-12, 2018.
C. Wang, J. Lv, J. A. Coulter, J. Xie, J. Yu, J. Li and Y. Gan, “Slow-release fertilizer improves the growth, quality, and nutrient utilization of wintering Chinese chives (Allium tuberosum Rottler ex Spreng.),” Agronomy, vol. 10, no. 3, p. 381,
X. Li and Z. Li, “Global trends and current advances in slow/controlled-release fertilizers: a bibliometric analysis from 1990 to 2023,” Agriculture, vol. 14, no. 9, p. 1502, 2024.
E. Khosla, R. Dharavath and R. Priya, “Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression,” Environment, Development and Sustainability, vol. 22, no. 6, pp. 5687-5708, 2020.
K. K. Paidipati, C. Chesneau, B. M. Nayana, K. R. Kumar, K. Polisetty and C. Kurangi, “Prediction of rice cultivation in India—support vector regression approach with various kernels for non-linear patterns,” AgriEngineering, vol. 3, no. 2,
pp. 182-198, 2021.
P. Xu, G. Li, Y. Zheng, J. C. Fung, A. Chen, Z. Zeng and B. Z. Houlton, “Fertilizer management for global ammonia emission reduction,” Nature, vol. 626, no. 8000, pp. 792-798, 2024.
J. Li and X. D. Yang, “Temperature sensitivity, nutrient release performance, and temperature sensitive mechanism of temperature-sensitive slow/controlled-release fertilizers: a comprehensive research review,” Journal of Plant Nutrition and Fertilizers, 2024.
J. Zhang, W. Xiao, Y. Li and S. Zhang, “Residual compensation extreme learning machine for regression,” Neurocomputing, vol. 311, pp. 126-136, Oct. 2018.
P. Hou, C. Shi, T. Xu, L. Xue, J. Wang, Q. Liu and L. Yang, “Long-term fertilizer optimization with slow-release and organic fertilizers improves N use efficiency and soil bacterial populations in paddies,” Journal of Soil Science and Plant Nutrition, vol. 23, no. 4, pp. 6088-6100, 2023.
W. Vatanee, L. Sumetee, K. Irie and C. Somchai, “Growth and yield-related traits of Khao Dawk Mali 105 (KDML105) rice in paddy and upland conditions,” J. ISSAAS , vol.29, no. 1, pp. 1-11, 2023.
J. Ke, X. Xing, G. Li, Y. Ding, F. Dou, S. Wang and L. Chen, “Effects of different controlled release nitrogen fertilisers on ammonia volatilisation, nitrogen use efficiency and yield of blanket-seedling machine-transplanted rice,” Field Crops Research, vol. 205, pp. 147-156, 2017.
L. Yang, Z. Ren, Y. Wang and H. Dong, “A robust regression framework with Laplace kernel induced loss,” Neural Computation, vol. 29, no. 11, pp. 3014-3039, 2017.
J. Pant, R. P. Pant, M. K. Singh, D. P. Singh and H. Pant, “Analysis of agricultural crop yield prediction using statistical techniques of machine learning,” Materials Today: Proceedings, vol. 46, pp. 10922-10926, 2021.
H. Burdett and C. Wellen, “Statistical and machine learning methods for crop yield prediction in the context of precision agriculture,” Precision Agriculture, vol. 23, no. 5, pp. 1553-1574, 2022.
P. Salaisook, D. T. N. Huy, P. A. Dung, L. N. Nuong, N. T. Thuy and D. T. N. Hien, “Further analysis on organic agriculture and organic farming in case of Thailand agriculture and enhancing farmer productivity,” International Journal of Horticulture, Agriculture and Food Science (IJHAF), vol. 8, no. 1, pp. 14-18, 2024.
S. K. Das and G. K. Ghosh, “Developing biochar-based slow-release NPK fertilizer for controlled nutrient release and its impact on soil health and yield,” Biomass Conversion and Biorefinery, vol. 13, no. 14, pp. 13051-13063, 2023.
H. M. El Sharkawi et al., “Biochar-ammonium phosphate as an uncoated-slow release fertilizer in sandy soil,” Biomass Bioenergy, vol. 117, pp. 154-160, 2018.
C. Guo et al., “Producing more grain yield of rice with less ammonia volatilization and greenhouse gases emission using slow/controlled release urea,” Environmental Science and Pollution Research, vol. 26, pp. 2569-2579, 2021.
Y. Gao et al., “Controlled-release nitrogen fertilizer driving the symbiosis of microbial communities,” Field Crop Research, vol. 289, p. 108712, 2022.
G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2016.
S. Ding, X. Xu and R. Nie, “Extreme learning machine and its applications,” Neural Computing and Applications, vol. 25, pp. 549-556, 2014.
J. Wang, S. Lu, S. H. Wang and Y. D. Zhang, “A review on extreme learning machine,” Multimedia Tools and Applications, vol. 81, no. 29, pp. 41611-41660, 2022.
J. Zhang, W. Xiao, Y. Li and S. Zhang, “Residual compensation extreme learning machine for regression,” Neurocomputing, vol. 311, pp. 126-136, 2018.
Y. Chen, X. Xie, T. Zhang, J. Bai and M. Hou, “A deep residual compensation extreme learning machine and applications,” Journal of Forecasting, vol. 39, no. 6, pp. 986-999, 2020.
J. Ferdous Pet al., “Long-term fertilizer optimization with slow-release and organic fertilizers,” Journal of Soil Science and Plant Nutrition, vol. 23, pp. 6088-6100, 2023.
K. Nagasubramanian et al., “Hyperspectral imaging and machine learning for crop stress detection and management,” Current Opinion in Biotechnology, vol. 64, pp. 162-171, 2020.
J. Zhang et al., “Comparative study of ELM, SVM, and random forest for crop yield prediction using remote sensing data,” Remote Sensing, vol. 15, no. 3, p. 587, 2023.
V. Nguyen, S. Jeong, J. Ko, C. Ng and J. Yeom, “Mathematical integration of remotely-sensed information into a crop modelling process for mapping crop productivity,” Remote Sensing, vol. 11, no. 18, p. 2131, 2019.
H. Burdett and C. Wellen, “Statistical and machine learning methods for crop yield prediction in the context of precision agriculture,” Precision Agriculture, vol. 23, no. 5, pp. 1553-1574, 2022.
H. S. A. L. Kutubi, “On randomized complete block design,” International Journal of Sciences: Basic and Applied Research, pp. 230-243, 2021.
M. A. Hall, “Correlation-based feature selection for machine learning,” Doctoral dissertation, The University of Waikato, New Zealand, 1999.
N. Gopika and A. M. Kowshalaya M.E., “Correlation Based Feature Selection Algorithm for Machine Learning,” 2018 3rd International Con ference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp. 692-695, 2018.
N. Japkowicz and M. Shah, “Performance evaluation in machine learning,” in Machine Learning in Radiation Oncology: Theory and Applications, pp. 41-56, 2015.
I. G. Baird, “Going organic: Challenges for government-supported organic rice promotion and certification nationalism in Thailand,” World Development, vol. 173, p. 106421, 2024.
H. Willer, J. Tr´avn´ıˇcek, and S. Schlatter, The World of Organic Agriculture: Statistics and Emerging Trends 2024, FiBL, IFOAM - Organic International, 2024. [Online]. Available: https://orgprints.org/id/eprint/52272/