From Compost Intelligence to Farm Decisions: A Review of Explainable AI for Organic Manure Maturity and Management

References

[1] Abaker M, et al. Composting process monitoring and maturity prediction by spectroscopic measurement and PLS modelling. International Journal of Environmental Research. 2025. https://doi.org/10.1007/s41742-025-00753-3

[2] Abdi R, et al. Prediction compost criteria of organic wastes with biochar additive in in-vessel composting machine using ANFIS and ANN methods. Energy Reports. 2023. https://doi.org/10.1016/j.egyr.2023.01.001

[3] Cervelli E, et al. The landscape approach provides additional support for livestock manure management as demonstrated through the buffalo herd case study in Sele plain, Campania region. In: Proc. IEEE MetroAgriFor. 2021. https://doi.org/10.1109/MetroAgriFor52389.2021.9628616

[4] Dankwa DD, Boh MY, Clark OG. Measuring greenhouse gas emissions from composting: A comparative review of methods. Waste Management. 2026. https://doi.org/10.1016/j.wasman.2025.115250

[5] Dehghan A, et al. Machine learning-based prediction of the C/N ratio in municipal organic waste. Environmental Technology & Innovation. 2025. https://doi.org/10.1016/j.eti.2024.103977

[6] Fang B, et al. Artificial intelligence for waste management in smart cities: A review. Environmental Chemistry Letters. 2023. https://doi.org/10.1007/s10311-023-01604-3

[7] Goldan E, et al. Assessment of manure compost used as soil amendment—A review. Processes. 2023. https://doi.org/10.3390/pr11041167

[8] Goswami PK, et al. Greenhouse gas sequestration in poultry farming: Strategies for sustainable production and environmental impact mitigation. Poultry Science. 2026. https://doi.org/10.1016/j.psj.2026.106646

[9] Karimi S, et al. Machine learning based prediction of maturity and correlation analysis of additive treated cattle and sheep manure composts and vermi composts using machine learning algorithms. Agriculture. 2025. https://doi.org/10.3390/agriculture16080834

[10] Khandakar A, et al. Compost maturity prediction and gas emissions monitoring: A sensor-based and interpretable machine learning approach. Computers and Electrical Engineering. 2025. https://doi.org/10.1016/j.compeleceng.2025.110115

[11] Lazrak M, Ait Baddi G, Achemchem F, Ayour J, Zaidouni S, Chebli B. AI-driven and sensor-based composting in a connected bioreactor: A novel framework for predicting maturity and quality dynamics. Waste Management Bulletin. 2025. https://doi.org/10.1016/j.wmb.2025.100254

[12] Li Y, et al. Identification and interaction analysis of environmental and microbial drivers of CO2-equivalent emissions in green waste composting using explainable machine learning. Environmental Technology & Innovation. 2026. https://doi.org/10.1016/j.eti.2026.104819

[13] Lyberatos V, Lyberatos G. Application of machine learning algorithms to predict composting process performance. Proceedings. 2025. https://doi.org/10.3390/proceedings2025121003

[14] Oueld Lhaj M, et al. Sustainable agriculture through agricultural waste management: A comprehensive review of composting's impact on soil health in Moroccan agricultural ecosystems. Agriculture. 2025. https://doi.org/10.3390/agriculture14122356

[15] Perez-San Martín A, et al. Spectroscopy techniques for monitoring the composting process: A review. Agronomy. 2023. https://doi.org/10.3390/agronomy13092245

[16] Rastogi M, Nandal M, Khosla B. Microbes as vital additives for solid waste composting. Heliyon. 2020. https://doi.org/10.1016/j.heliyon.2020.e03343

[17] Scotto di Perta E, et al. Is the biochar an effective floating cover for manure storage to reduce ammonia emissions, adsorbing nitrogen at the same time? In: Proc. IEEE MetroAgriFor. 2020. https://doi.org/10.1109/MetroAgriFor50201.2020.9277602

[18] Shi S, et al. Machine learning-based prediction of compost maturity and identification of key parameters during manure composting. Bioresource Technology. 2025. https://doi.org/10.1016/j.biortech.2024.132024

[19] Szulc W, et al. Possibilities of using organic waste after biological and physical processing—An overview. Processes. 2021. https://doi.org/10.3390/pr9091501

[20] Xie Y, et al. The evolution of nutrient and microbial composition and maturity during the composting of different plant-derived wastes. Biology. 2025. https://doi.org/10.3390/biology14030268

@article{1723833,
      author = {Akanksha Bhaskar Shewale, Jyoti Tale},
      title = {From Compost Intelligence to Farm Decisions: A Review of Explainable AI for Organic Manure Maturity and Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {1094-1104},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723833.pdf},
      abstract = {Composting of animal manure and agricultural organic waste represents a significant strategy for nutrient recycling, soil improvement and sustainable waste management. However, an accurate evaluation of the compost maturity and quality is a challenging task due to the number of physical, chemical, biological and environmental factors involved in the process. Conventional approaches usually rely on laboratory analyses and individual maturity indicators which can often be time-consuming and difficult to integrate with practical decision support systems. Thus, recent studies have explored the use of machine learning (ML), artificial intelligence (AI), spectroscopy, the Internet of Things (IoT), and explainable artificial intelligence (XAI) to predict compost maturity, quality parameters and environmental emissions. This paper aims to review 20 studies published between 2020 and 2026 on manure composting, compost maturity prediction, spectroscopic monitoring, microbial additives, greenhouse-gas emissions, artificial intelligence, sensor-based systems and agricultural waste management. The reviewed literature revealed that Random Forest, Extra Trees, XGBoost, AdaBoost, ANN and adaptive neuro-fuzzy inference systems can deliver highly accurate prediction results under certain experimental conditions. Nevertheless, most of the studies suffer from small dataset size issues, heterogeneous data, lack of feedstock variety, controlled environment, limited external validation, inconsistent compost maturity definitions and limited comparison between different findings. While the recent advances in SHAP and LIME interpretation techniques facilitate better understanding of model behaviour, there is still a long way to go before such findings can be used to make actionable agricultural recommendations. The review identifies a research gap for an integrated, reproducible, and explainable decision-support framework that combines compost maturity prediction with interpretation and practical manure-management recommendations using publicly available data. The identified gaps motivate the proposed FarmMind framework for explainable organic-manure decision support.},
      keywords = {agricultural waste; compost maturity; decision support system; explainable AI; organic manure; SHAP},
      month = {October},
  }