Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements
Harvested forage is the main raw feed for ruminant animals in Sweden, and is commonly cultivated in mixed stands of legume and grass species. The fraction of legume on a dry matter basis, known as botanical composition (BC) is a very important indicator of forage quality. In this study, hyperspectra...
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Elsevier
2024-12-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154324003569 |
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| author | Junxiang Peng Maryam Rahimi Jahangirlou Julien Morel Zhenjiang Zhou David Parsons |
| author_facet | Junxiang Peng Maryam Rahimi Jahangirlou Julien Morel Zhenjiang Zhou David Parsons |
| author_sort | Junxiang Peng |
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| description | Harvested forage is the main raw feed for ruminant animals in Sweden, and is commonly cultivated in mixed stands of legume and grass species. The fraction of legume on a dry matter basis, known as botanical composition (BC) is a very important indicator of forage quality. In this study, hyperspectral imaging and near-infrared spectrometer (NIRS) based methods were used to estimate BC, to overcome the shortcomings of hand separation, which is time and resource consuming. Timothy and red clover mix samples were collected from different harvests in 2017–2019 from multiple sites in Northern Sweden and hand separated. The samples were synthetically mixed to 11 different BC levels, i.e., 0–100 % clover content. Two different instruments (Specim shortwave infrared (SWIR) hyperspectral imaging system and Foss 6500 spectrometer) were used to collect spectral data of samples milled to two levels of coarseness. Three different regression analyses: partial least squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR), were used to build BC estimation models. The effects of the milling particle sizes and the different instruments on the performances of the models were compared. The data from second harvest in 2019 were used for independent validation as evaluation, and the rest of data were randomly split for model calibration (75 %) and validation (25 %). The models were iteratively run 1000 times with different splits, to check the effect from the splitting of calibration and validation datasets. Among different regression analyses, PLSR performed best, with mean Nash-Sutcliffe efficiency (NSE) for model evaluation from 0.76 to 0.87, varying for different instruments and milling sizes. Finer milling made the model accuracies slightly higher. This study developed quick and robust methods to determine the BC of timothy grass and red clover mixtures, which can provide useful information for farmers or researchers. |
| format | Article |
| id | doaj-art-0f22f0beaebd4c0eacaed69ee6d36995 |
| institution | Kabale University |
| issn | 2666-1543 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-0f22f0beaebd4c0eacaed69ee6d369952024-12-16T05:37:21ZengElsevierJournal of Agriculture and Food Research2666-15432024-12-0118101319Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurementsJunxiang Peng0Maryam Rahimi Jahangirlou1Julien Morel2Zhenjiang Zhou3David Parsons4Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden; Corresponding author.Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden; Department of Agroecology, Aarhus University, 8830 Tjele, DenmarkEuropean Commission, Joint Research Centre, 21027 Ispra, ItalyCollege of Biosystems Engineering and Food Science, Zhejiang University, 310058 Hangzhou, ChinaDepartment of Crop Production Ecology, Swedish University of Agricultural Sciences, 90183 Umeå, SwedenHarvested forage is the main raw feed for ruminant animals in Sweden, and is commonly cultivated in mixed stands of legume and grass species. The fraction of legume on a dry matter basis, known as botanical composition (BC) is a very important indicator of forage quality. In this study, hyperspectral imaging and near-infrared spectrometer (NIRS) based methods were used to estimate BC, to overcome the shortcomings of hand separation, which is time and resource consuming. Timothy and red clover mix samples were collected from different harvests in 2017–2019 from multiple sites in Northern Sweden and hand separated. The samples were synthetically mixed to 11 different BC levels, i.e., 0–100 % clover content. Two different instruments (Specim shortwave infrared (SWIR) hyperspectral imaging system and Foss 6500 spectrometer) were used to collect spectral data of samples milled to two levels of coarseness. Three different regression analyses: partial least squares regression (PLSR), support vector regression (SVR) and random forest regression (RFR), were used to build BC estimation models. The effects of the milling particle sizes and the different instruments on the performances of the models were compared. The data from second harvest in 2019 were used for independent validation as evaluation, and the rest of data were randomly split for model calibration (75 %) and validation (25 %). The models were iteratively run 1000 times with different splits, to check the effect from the splitting of calibration and validation datasets. Among different regression analyses, PLSR performed best, with mean Nash-Sutcliffe efficiency (NSE) for model evaluation from 0.76 to 0.87, varying for different instruments and milling sizes. Finer milling made the model accuracies slightly higher. This study developed quick and robust methods to determine the BC of timothy grass and red clover mixtures, which can provide useful information for farmers or researchers.http://www.sciencedirect.com/science/article/pii/S2666154324003569ForageBotanical compositionHyperspectral imagingNIRSParticle size |
| spellingShingle | Junxiang Peng Maryam Rahimi Jahangirlou Julien Morel Zhenjiang Zhou David Parsons Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements Journal of Agriculture and Food Research Forage Botanical composition Hyperspectral imaging NIRS Particle size |
| title | Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements |
| title_full | Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements |
| title_fullStr | Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements |
| title_full_unstemmed | Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements |
| title_short | Estimation of botanical composition of forage crops using laboratory-based hyperspectral imaging and near-infrared spectrometer measurements |
| title_sort | estimation of botanical composition of forage crops using laboratory based hyperspectral imaging and near infrared spectrometer measurements |
| topic | Forage Botanical composition Hyperspectral imaging NIRS Particle size |
| url | http://www.sciencedirect.com/science/article/pii/S2666154324003569 |
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