Evaluation of a low-cost multispectral imaging system for rapid estimation of chemical composition in tropical grasses
Palabras clave:
Multispectral spectroscopy, chemical compounds, livestock forage, Urochloa, MegathyrsusResumen
The evaluation of tropical forage nutritional quality using conventional analytical methods is costly and time-consuming, limiting its application in livestock systems. The objective of this work was to develop and validate a low-cost multispectral image spectroscopy system (AFT camera), to estimate the chemical composition of tropical grasses. A total of 332 forage samples, including 293 Urochloa spp. and 39 Megathyrsus maximus (Jacq.) B.K.Simon & S.W.L.Jacobs, were analyzed using standard laboratory methods and multispectral image regression analysis. Calibration models showed coefficients of determination (R²) ranging from 0.36 to 0.98, while validation models exceeded 0.60 for neutral detergent fiber and 0.70 for crude protein and digestibility. No significant differences (p > 0.05) were detected between observed and predicted values. The AFT system demonstrated potential as a rapid and low-cost tool for forage quality assessment, although calibration performance varied according to forage species and chemical component evaluated.
Referencias
Abreu, L. F., Lana, Â. M. Q., Climaco, L. C., Matrangolo, W. J. R., Barbosa, E. P., Da Silva, K. T., Rowntree, J. E., Da Silva, E. A., & Simeone, M. L. F. (2023). Near-infrared spectroscopy and chemometrics methods to predict the chemical composition of Cratylia argentea. Agronomy, 13(10), 2525. https://doi.org/10.3390/agronomy13102525
Ancin-Murguzur, F. J., Brown, A. G., Clarke, C., Sjøgren, P., Svendsen, J. I., & Alsos, I. G. (2020). How well can near infrared reflectance spectroscopy (NIRS) measure sediment organic matter in multiple lakes? Journal of Paleolimnology, 64, 59-69. https://doi.org/10.1007/s10933-020-00121-5
Andueza, D., Picard, F., Dozias, D., & Aufrère, J. (2018). Fecal near-infrared reflectance spectroscopy prediction of the feed value of temperate forages for ruminants and some parameters of the chemical composition of feces: Efficiency of four calibration strategies. Applied Spectroscopy, 71(9), 2164-2176. https://doi.org/10.1177/0003702817712740
Ariyo, S. O., Adeyemi, A. A., Adewale, A. S., Alao, O. J., Aderemi, O. V. O., Sadiat, A. A., & Esther, O. T. (2025). Interactive effects of nitrogen fertilization and Harvest age on the nutritional composition of Brachiaria ruziziensis. Journal of Soil Plant and Environment, 4(2), 1-17. https://doi.org/10.56946/jspae.v4i2.661
Basurto Gutiérrez, R., Ramírez Rodríguez, E., & Mariscal Landín, G. (2025). Estimación de la composición química de granos y pastas proteicas mediante espectroscopia (NIRS-FTIR). Revista Mexicana de Ciencias Pecuarias, 16(2), 428-445. https://doi.org/10.22319/rmcp.v16i2.6637
Cabral, Í. dos S., Oliveira, S. S., Azevêdo, J. A. G., Souza, L. L., De Lima, R. F., Lopes, C. da C., Otani, F. S., Reis, S. M., & Sousa, C. A. F. (2020). Ruminal fermentation kinetics of by-products using the semi-automatic technique of in-vitro gas production. Revista Brasileira de Saúde e Produção Animal, 21(01-08), e2121242020, https://doi.org/10.1590/s1519-99402121242020
Caradus, J. R., & Chapman, D. F. (2024). Evaluating pasture forage plant breeding achievements: a review. New Zealand Journal of Agricultural Research, 68(6), 1146-1220. https://doi.org/10.1080/00288233.2024.2395370
Charmley, E., Williams, S. R. O., Moate, P. J., Hegarty, R. S., Herd, R. M., Oddy, V. H., Reyenga, P., Staunton, K. M., Anderson, A., & Hannah, M. C. (2016). A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science, 56(3), 169-180. https://doi.org/10.1071/an15365
Costa, C. M., Difante, G. S., Costa, A. B. G., Gurgel, A. L. C., Ferreira, M. A., Jr, & Santos, G. T. (2021). Grazing intensity as a management strategy in tropical grasses for beef cattle production: a meta-analysis. Animal, 15(4), 100192. https://doi.org/10.1016/j.animal.2021.100192
Dias, C. S. A. M. M., Nunes, H. P. B., & Borba, A. E. S. (2023). Influence of the physical properties of samples in the use of NIRS to predict the chemical composition and gas production kinetic parameters of corn and grass silages. Fermentation, 9(5), 418. https://doi.org/10.3390/fermentation9050418
Erdaw, M. M. (2023). Contribution, prospects, and trends of livestock production in sub-Saharan Africa: a review. International Journal of Agricultural Sustainability, 21(1), 2247776. https://doi.org/10.1080/14735903.2023.2247776
Fakude, S. B., Soundy, P., & Sosibo, N. Z. (2025). Effect of selected factors on the prediction accuracy of plant-available phosphorus and potassium: A global meta-analysis for infrared spectroscopy protocol. Agronomy, 15(12), 2771. https://doi.org/10.3390/agronomy15122771
Gamon, J. A., Somers, B., Malenovský, Z., Middleton, E. M., Rascher, U., & Schaepman, M. E. (2019). Assessing vegetation function with imaging spectroscopy. Surveys in Geophysics, 40, 489-513. https://doi.org/10.1007/s10712-019-09511-5
Ghosh, S., & Sahu, T. N. (2023). Targeting zero hunger to ensure sustainable development: Insights from a panel structure. Sustainable Development, 31(4), 2814-2825. https://doi.org/10.1002/sd.2549
Hughes, M. P., Mlambo, V., Lallo, C. H. O., Basha, N. A. D., Nsahlai, I. V., & Jennings, P. G. A. (2017). Accuracy of two optical chlorophyll meters in predicting chemical composition and in vitro ruminal organic matter degradability of Brachiaria hybrid, Megathyrsus maximus, and Paspalum atratum. Animal Nutrition, 3(1), 67-76. https://doi.org/10.1016/j.aninu.2016.10.002
Jarque-Bascuñana, L., Bartolomé, J., Serrano, E., Espunyes, J., Garel, M., Calleja Alarcón, J. A., López-Olvera, J. R., & Albanell, E. (2021). Near infrared reflectance spectroscopy analysis to predict diet composition of a mountain ungulate species. Animals, 11(5), 1449. https://doi.org/10.3390/ani11051449
Kleen, J. L., & Guatteo, R. (2023). Precision livestock farming: What does it contain and what are the perspectives? Animals, 13(5), 779. https://doi.org/10.3390/ani13050779
Latimer, G. W. Jr. (Ed.). (2023). Official Methods of Analysis of AOAC International. Association of Official Analytical Chemists. https://doi.org/10.1093/9780197610145.002.001
Luo, X., Keenan, T. F., Chen, J. M., Croft, H., Prentice, I. C., Smith, N. G., Walker, A. P., Wang, H., Wang, R., Xu, C., & Zhang, Y. (2021). Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nature Communications, 12, 4866. https://doi.org/10.1038/s41467-021-25163-9
Monrroy, M., Gutiérrez, D., Miranda, M., Hernández, K., & García, J. R. (2017). Determination of Brachiaria spp. for age quality by near-infrared spectroscopy and partial least squares regression. Journal of the Chilean Chemical Society, 62(2), 3472-3477. https://doi.org/10.4067/s0717-97072017000200010
Namazzi, C., Sserumaga, J. P., Mugerwa, S., Kyalo, M., Mutai, C., Mwesigwa, R., Djikeng, A., & Ghimire, S. (2020). Genetic diversity and population structure of Brachiaria (syn. Urochloa) ecotypes from Uganda. Agronomy, 10(8), 1193. https://doi.org/10.3390/agronomy10081193
Parrini, S., Acciaioli, A., Crovetti, A., & Bozzi, R. (2018). Use of FT-NIRS for determination of chemical components and nutritional value of natural pasture. Italian Journal of Animal Science, 17(1), 87-91. https://doi.org/10.1080/1828051X.2017.1345659
Paul, N., Sunil, G. C., Horvath, D., & Sun, X. (2025). Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions. Computers and Electronics in Agriculture, 229, 109734. https://doi.org/10.1016/j.compag.2024.109734
Perry, L. A., Jassim, R. A., Gaughan, J. B., & Tomkins, N. W. (2017). Effect of feeding forage characteristic of wet- or dry-season tropical C4 grass in northern Australia, on methane production, intake and rumen outflow rates in Bos indicus steers. Animal Production Science, 57(10), 2033-2041. https://doi.org/10.1071/an15314
R Core Team. (2019). The R Project for Statistical Computing. https://www.r-project.org/
Radočaj, D., Plaščak, I., & Jurišić, M. (2023). Global navigation satellite systems as state-of-the-art solutions in precision agriculture: A review of studies indexed in the Web of Science. Agriculture, 13(7), 1417. https://doi.org/10.3390/agriculture13071417
Rao, I., Peters, M., Castro, A., Schultze-Kraft, R., White, D., Fisher, M., Miles, J., Lascano, C., Blummel, M., Bungenstab, D., Tapasco, J., Hyman, G., Bolliger, A., Paul, B., Van Der Hoek, R., Maass, B., Tiemann, T., Cuchillo, M., Douxchamps, S., Villanueva, C., Rincón, Á., Ayarza, M., Rosenstock, T., Subbarao, G., Arango, J., Cardoso, J., Worthington, M., Chirinda, N., Notenbaert, A., Jenet, A., Schmidt, A., Vivas, N., Lefroy, R., Fahrney, K., Guimarães, E., Tohme, J., Coo, S., Herrero, M., Chacón, M., Serchinger, T., & Rudel, T. (2015). LivestockPlus - The sustainable intensification of forage-based agricultural systems to improve livelihoods and ecosystem services in the tropics. Tropical Grasslands-Forrajes Tropicales, 3(2), 59-82. https://doi.org/10.17138/tgft(3)59-82
Robles-Zazueta, C. A., Pinto, F., Molero, G., Foulkes, M. J., Reynolds, M. P., & Murchie, E. H. (2022). Prediction of photosynthetic, biophysical, and biochemical traits in wheat canopies to reduce the phenotyping bottleneck. Frontiers in Plant Science, 13, 828451. https://doi.org/10.3389/fpls.2022.828451
Schindelin, J., Rueden, C. T., Hiner, M. C., & Eliceiri, K. W. (2015). The ImageJ ecosystem: An open platform for biomedical image analysis. Molecular Reproduction and Development, 82(7-8), 518-529. https://doi.org/10.1002/mrd.22489
Tan, P., Liu, H., Zhao, J., Gu, X., Wei, X., Zhang, X., Ma, N., Johnston, L. J., Bai, Y., Zhang, W., Nie, C., & Ma, X. (2021). Amino acids metabolism by rumen microorganisms: Nutrition and ecology strategies to reduce nitrogen emissions from the inside to the outside. The Science of the Total Environment, 800, 149596. https://doi.org/10.1016/j.scitotenv.2021.149596
Thomson, A. L., Humphries, D. J., Rymer, C., Archer, J. E., Grant, N. W., & Reynolds, C. K. (2018). Assessing the accuracy of current near infra-red reflectance spectroscopy analysis for fresh grass-clover mixture silages and development of new equations for this purpose. Animal Feed Science and Technology, 239, 94-106. https://doi.org/10.1016/j.anifeedsci.2018.03.009
Torres-Lugo, R. B., Solorio-Sánchez, F. J., Avilés, L. R. Y., Ku-Vera, J. C., Aguilar-Pérez, C. F., & Santillano-Cázares, J. (2022). Productivity, morphology and chemical composition of Brachiaria spp. ecotypes, under two solar illumination intensities, in Yucatan, Mexico. Agronomy, 12(11), 2634. https://doi.org/10.3390/agronomy12112634
Van Soest, P. J., Robertson, J. B., & Lewis, B. A. (1991). Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science, 74(10), 3583-3597. https://doi.org/10.3168/jds.s0022-0302(91)78551-2
Xiong, D., Chen, J., Yu, T., Gao, W., Ling, X., Li, Y., Peng, S., & Huang, J. (2015). SPAD-based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteristics. Scientific Reports, 5, 13389. https://doi.org/10.1038/srep13389
Zhao, Y., Zhao, Z., Shan, P., Peng, S., Yu, J., & Gao, S. (2019). Calibration transfer based on affine invariance for NIR without transfer standards. Molecules, 24(9), 1802. https://doi.org/10.3390/molecules24091802
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Julián Arroyave-Jaramillo, Daniel M. Pineda-Tobón, Luis A. Giraldo-Valderrama, Juan C. Pérez-Naranjo, Luis A. Chel-Guerrero, David Betancur-Ancona

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
