Moisture prediction in chicken litter using hyperspectral data and machine learning
Efficient moisture monitoring in chicken litter is essential for sustainable poultry waste management and its application as an organic fertilizer. Conventional methods like oven drying are destructive, time-consuming, and unsuitable for real-time assessment. While hyperspectral imaging (HSI) has be...
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| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002916 |
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| author | Ahmad Tulsi Abdul Momin Victoria Ayres |
| author_facet | Ahmad Tulsi Abdul Momin Victoria Ayres |
| author_sort | Ahmad Tulsi |
| collection | DOAJ |
| description | Efficient moisture monitoring in chicken litter is essential for sustainable poultry waste management and its application as an organic fertilizer. Conventional methods like oven drying are destructive, time-consuming, and unsuitable for real-time assessment. While hyperspectral imaging (HSI) has been widely applied in agricultural sensing, its use for non-destructive moisture estimation in chicken litter remains largely unexplored. This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. We employed two modeling approaches: Partial Least Squares Regression (PLSR) and Extreme Gradient Boosting (XGBoost), to assess performance using raw spectral data and under three preprocessing techniques designed to enhance data quality: Moving Average (MA), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). XGBoost performed best on raw data (R² = 0.97, RMSE = 1.01), while PLSR improved notably with preprocessing, especially MA (R² = 0.92, RMSE = 1.56). These results reveal a key insight: preprocessing enhances linear model accuracy but may suppress critical non-linear patterns needed by tree-based models like XGBoost. Additionally, spatial visualization of moisture distribution across composting stages demonstrated the added value of HSI in characterizing heterogeneous organic materials. This study introduces a novel framework integrating HSI and machine learning for fast, non-destructive moisture assessment in poultry litter. It establishes a baseline for future work exploring deep learning, feature importance analysis, and multi-modal fusion to further improve accuracy and generalizability across diverse agricultural waste conditions. |
| format | Article |
| id | doaj-art-4df3af79775a4958b5b0bc138e44c588 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-4df3af79775a4958b5b0bc138e44c5882025-08-20T02:32:23ZengElsevierSmart Agricultural Technology2772-37552025-08-011110105810.1016/j.atech.2025.101058Moisture prediction in chicken litter using hyperspectral data and machine learningAhmad Tulsi0Abdul Momin1Victoria Ayres2School of Agriculture, Tennessee Technological University, Cookeville, TN 38505, USA; School of Environmental Studies, Tennessee Technological University, Cookeville, TN 38505, USASchool of Agriculture, Tennessee Technological University, Cookeville, TN 38505, USA; Corresponding author.School of Agriculture, Tennessee Technological University, Cookeville, TN 38505, USAEfficient moisture monitoring in chicken litter is essential for sustainable poultry waste management and its application as an organic fertilizer. Conventional methods like oven drying are destructive, time-consuming, and unsuitable for real-time assessment. While hyperspectral imaging (HSI) has been widely applied in agricultural sensing, its use for non-destructive moisture estimation in chicken litter remains largely unexplored. This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. We employed two modeling approaches: Partial Least Squares Regression (PLSR) and Extreme Gradient Boosting (XGBoost), to assess performance using raw spectral data and under three preprocessing techniques designed to enhance data quality: Moving Average (MA), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). XGBoost performed best on raw data (R² = 0.97, RMSE = 1.01), while PLSR improved notably with preprocessing, especially MA (R² = 0.92, RMSE = 1.56). These results reveal a key insight: preprocessing enhances linear model accuracy but may suppress critical non-linear patterns needed by tree-based models like XGBoost. Additionally, spatial visualization of moisture distribution across composting stages demonstrated the added value of HSI in characterizing heterogeneous organic materials. This study introduces a novel framework integrating HSI and machine learning for fast, non-destructive moisture assessment in poultry litter. It establishes a baseline for future work exploring deep learning, feature importance analysis, and multi-modal fusion to further improve accuracy and generalizability across diverse agricultural waste conditions.http://www.sciencedirect.com/science/article/pii/S2772375525002916Moisture predictionSpectral data analysisNon-destructive testingPoultry waste management |
| spellingShingle | Ahmad Tulsi Abdul Momin Victoria Ayres Moisture prediction in chicken litter using hyperspectral data and machine learning Smart Agricultural Technology Moisture prediction Spectral data analysis Non-destructive testing Poultry waste management |
| title | Moisture prediction in chicken litter using hyperspectral data and machine learning |
| title_full | Moisture prediction in chicken litter using hyperspectral data and machine learning |
| title_fullStr | Moisture prediction in chicken litter using hyperspectral data and machine learning |
| title_full_unstemmed | Moisture prediction in chicken litter using hyperspectral data and machine learning |
| title_short | Moisture prediction in chicken litter using hyperspectral data and machine learning |
| title_sort | moisture prediction in chicken litter using hyperspectral data and machine learning |
| topic | Moisture prediction Spectral data analysis Non-destructive testing Poultry waste management |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002916 |
| work_keys_str_mv | AT ahmadtulsi moisturepredictioninchickenlitterusinghyperspectraldataandmachinelearning AT abdulmomin moisturepredictioninchickenlitterusinghyperspectraldataandmachinelearning AT victoriaayres moisturepredictioninchickenlitterusinghyperspectraldataandmachinelearning |