Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms
Nitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pine...
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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/S266615432400245X |
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| author | Jorge Enrique Chaparro José Edinson Aedo Felipe Lumbreras Ruiz |
| author_facet | Jorge Enrique Chaparro José Edinson Aedo Felipe Lumbreras Ruiz |
| author_sort | Jorge Enrique Chaparro |
| collection | DOAJ |
| description | Nitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pineapple crops from data from multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); in situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, soil moisture, wind speed and direction, as well as SPAD values indicating leaf chlorophyll content.Total nitrogen (TN) values were taken from leaf tissue samples, which were then analyzed in a laboratory. To introduce nitrogen variability, a complete randomized block experimental design was implemented, applying five different treatments in five blocks, each with 12 replications, during a 6-month period in a pineapple crop located in Tauramena, Colombia. To address the inherent variability in the agricultural and environmental data, dimensionality was reduced using Principal Component Analysis (PCA). In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. On the first sampling date, XGBoost achieved an R2 of 86.98 %, being the highest. On the following dates, MLP achieved a R2 of 59.11 % on the second date; XGBoost achieved a R2 of 68.00 % on the third date, and on the last date, MLP achieved a R2 of 69.4 %. These results indicate that the integration of data from multiple sources and the use of machine learning models could greatly improve the precision of nitro-gen (N) diagnostics in pineapple crops, especially in real-time applications. These findings highlight the promising potential of developing machine learning models that integrate multisensor data fusion for various applications in agriculture. |
| format | Article |
| id | doaj-art-a314b6db5bea4938976bfcedb7da6d86 |
| institution | Kabale University |
| issn | 2666-1543 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-a314b6db5bea4938976bfcedb7da6d862024-12-16T05:37:06ZengElsevierJournal of Agriculture and Food Research2666-15432024-12-0118101208Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platformsJorge Enrique Chaparro0José Edinson Aedo1Felipe Lumbreras Ruiz2University of Antioquia, Faculty of Engineering, Research Group on Embedded Systems and Computational Intelligence - SISTEMIC, Medellin, Antioquia, 050023, Colombia; Center for Technology Development and Innovation in TIC, NetworkTIC, Yopal, Casanare, 850001, Colombia; International University of the American Tropics, Unitropico, Yopal, Casanare, 850001, Colombia; Corresponding author. University of Antioquia, Faculty of Engineering, Research Group on Embedded Systems and Computational Intelligence - SISTEMIC, Colombia.University of Antioquia, Faculty of Engineering, Research Group on Embedded Systems and Computational Intelligence - SISTEMIC, Medellin, Antioquia, 050023, ColombiaComputer Vision Center, Computer Science Deptartment, Universitat Autònoma de Barcelona, Barcelona. Edifici O, Campus UAB, Bellaterra, 08193, SpainNitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pineapple crops from data from multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); in situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, soil moisture, wind speed and direction, as well as SPAD values indicating leaf chlorophyll content.Total nitrogen (TN) values were taken from leaf tissue samples, which were then analyzed in a laboratory. To introduce nitrogen variability, a complete randomized block experimental design was implemented, applying five different treatments in five blocks, each with 12 replications, during a 6-month period in a pineapple crop located in Tauramena, Colombia. To address the inherent variability in the agricultural and environmental data, dimensionality was reduced using Principal Component Analysis (PCA). In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. On the first sampling date, XGBoost achieved an R2 of 86.98 %, being the highest. On the following dates, MLP achieved a R2 of 59.11 % on the second date; XGBoost achieved a R2 of 68.00 % on the third date, and on the last date, MLP achieved a R2 of 69.4 %. These results indicate that the integration of data from multiple sources and the use of machine learning models could greatly improve the precision of nitro-gen (N) diagnostics in pineapple crops, especially in real-time applications. These findings highlight the promising potential of developing machine learning models that integrate multisensor data fusion for various applications in agriculture.http://www.sciencedirect.com/science/article/pii/S266615432400245XMultispectral imagingUnmanned aerial vehicle (UAV)Internet of Things IoTPredictive modelsSensors in the cropImage processing |
| spellingShingle | Jorge Enrique Chaparro José Edinson Aedo Felipe Lumbreras Ruiz Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms Journal of Agriculture and Food Research Multispectral imaging Unmanned aerial vehicle (UAV) Internet of Things IoT Predictive models Sensors in the crop Image processing |
| title | Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms |
| title_full | Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms |
| title_fullStr | Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms |
| title_full_unstemmed | Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms |
| title_short | Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms |
| title_sort | machine learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and internet of things iot platforms |
| topic | Multispectral imaging Unmanned aerial vehicle (UAV) Internet of Things IoT Predictive models Sensors in the crop Image processing |
| url | http://www.sciencedirect.com/science/article/pii/S266615432400245X |
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