Open Access
Issue
EPJ Web Conf.
Volume 336, 2025
International Conference on Sustainable Development in Advanced Materials, Manufacturing, and Industry 4.0 (INSDAM’25)
Article Number 03005
Number of page(s) 13
Section Industry 4.0
DOI https://doi.org/10.1051/epjconf/202533603005
Published online 26 September 2025
  1. J. Singh, S. Kalamdhad, Assessment of biochar and compost amendments on compost maturity of organic waste. Bioresour. Technol. 102, 2862–2868 (2011). https://doi.org/10.1016/j.biortech.2011.01.053 [Google Scholar]
  2. M.K. Awasthi, R.D. Pandey, A.O. Awasthi, N. Ravi, Composting of food waste: A review on its process and effects on soil properties. J. Environ. Manage. 258, 109928 (2020). https://doi.org/10.1016/j.jenvman.2019.109928 [Google Scholar]
  3. H. Huang, F. Yuan, C. Dong, J. Wang, Effects of urea and aeration on nitrogen transformation during composting of food waste. Bioresour. Technol. 104, 407–412 (2012). https://doi.org/10.1016/j.biortech.2011.10.045 [Google Scholar]
  4. M.P. Bernal, J. Alburquerque, R. Moral, Composting of animal manures and chemical criteria for compost maturity assessment: A review. Bioresour. Technol. 100, 5444–5453 (2009). https://doi.org/10.1016/j.biortech.2008.11.027 [Google Scholar]
  5. M.A. Vessey, Plant growth promoting rhizobacteria as biofertilizers. Plant Soil 255, 571–586 (2003). https://doi.org/10.1023/A:1026037216893 [CrossRef] [Google Scholar]
  6. R.L. Bhardwaj, R. Sharma, R. Sharma, P. Kaushal, Biofertilizers: A potential approach for sustainable agriculture development. Int. J. Curr. Microbiol. App. Sci. 3, 76–82 (2014). [Google Scholar]
  7. A. Rani, S. Ranjan, A machine learning approach for compost maturity prediction using physicochemical properties. Environ. Sci. Pollut. Res. 28, 14120–14130 (2021). https://doi.org/10.1007/s11356-021-14883-6 [Google Scholar]
  8. A.K. Srivastava, S. Kundu, S. Meena, K.K. Biswas, Agricultural sustainability: Microbial biofertilizers in rhizosphere management. Agriculture 11, 163 (2021). https://doi.org/10.3390/agriculture11020163 [Google Scholar]
  9. M.A. Iqbal, H. Javed, A. Hussain, M. Ahmad, Biofertilizers: A sustainable strategy for enhancing physical, chemical, and biological properties of soil. Innov. Agric. 1, 128697 (2023). [Google Scholar]
  10. S. Kumar, R. Awasthi, P. Singh, An overview of biofertilizers in crop production and stress management to improve yield and sustainability. Front. Plant Sci. 13, 930340 (2022). https://doi.org/10.3389/fpls.2022.930340 [Google Scholar]
  11. N. Wang, Y. Zhang, X. Chen, Predicting maturity and identifying key factors in organic waste composting using machine learning models. Bioresour. Technol. 360, 130663 (2024). https://doi.org/10.1016/j.biortech.2022.130663 [Google Scholar]
  12. S. Ding, Y. Zhao, F. Meng, Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model. Bioresour. Technol. 360, 127587 (2022). https://doi.org/10.1016/j.biortech.2022.127587 [Google Scholar]
  13. Y. Li, X. Wang, J. Zhang, Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning. Bioresour. Technol. 340, 125667 (2021). https://doi.org/10.1016/j.biortech.2021.125667 [Google Scholar]
  14. S. Kaur, G.S. Guru, P. Pandey, The role of machine learning in biofertilizer industry: From data analytics to predictive modelling. In: Metabolomics, Proteomics and Gene Editing Approaches in Biofertilizer Industry (Springer, Singapore, 2023), pp. 161–175. https://doi.org/10.1007/978-981-19-8060-0_7 [Google Scholar]
  15. H. Ali, S. Ali, G. Yaqub, Integrating ensemble machine learning and soil properties for rice yield prediction. Agric. Syst. 194, 103277 (2021). https://doi.org/10.1016/j.agsy.2021.103277 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.