Aplicación de índices vegetales (banda roja e infrarrojo cercano) en plantaciones de aguacate

Barahona, Anderson Mauricio Guerron; Viera, William; Cruz, Diego Fabricio Campaña; Rojas, Laura Vasquez; Salcedo, Carlos Lenin Montufar

doi:10.29166/siembra.v9i1.3371

SIEMBRA

2022

DOI: 10.29166/siembra.v9i1.3371

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Aplicación de índices vegetales (banda roja e infrarrojo cercano) en plantaciones de aguacate

Anderson Mauricio Guerron Barahona

William Viera

Diego Fabricio Campaña Cruz

et al.

Abstract: El aguacate es una fruta tradicional en la dieta de los ecuatorianos y requiere un manejo adecuado que garantice su producción. La implementación de nuevas alternativas tecnológicas, como utilización de índices de espectroscopia, permite obtener datos que al correlacionarlos optimizarán el manejo del cultivo. Esta investigación validó el uso de índices vegetales basados en la banda roja e infrarrojo cercano con el contenido de nitrógeno foliar. Se utilizó los índices vegetales: índice normalizado diferencial d… Show more

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2024

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Cited by 2 publications

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Estimation of citrus leaves’ nitrogen content by multispectral unmanned aerial vehicle remote sensing based on semi-supervised twin neural network regression

Li,

Du,

Zhang

et al. 2024

J. Appl. Rem. Sens.

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Accurate and timely monitoring of leaf nitrogen content (LNC) is crucial for citrus tree growth control and precision fertilization. Unmanned aerial vehicle (UAV) remote sensing can provide stronger support for rapid, flexible, large-scale monitoring of LNC distribution in orchards compared with traditional LNC chemical detection methods. However, time-consuming and high-cost sample collection leads to a limited number of samples for constructing remote sensing LNC estimation models, thereby limiting the accuracy of LNC estimation. We use the semi-supervised twin neural network regression (STNNR) model to make full use of a considerable portion of unlabeled and labeled data to extend the sample data for training the model and effectively improve the accuracy of nitrogen content estimation. First, multiple spectral indices are constructed using high-resolution multispectral UAV imagery, and the relationships of these spectral indices with the LNC are analyzed to select the spectral indices sensitive to changes in the LNC. Then, the unlabeled data are used for model training under the loop consistency condition. Three different loss functions are employed for training the STNNR model on the labeled and unlabeled data. Finally, the STNNR model is evaluated through comparison experiments with other supervised learning models. Results show that the STNNR model has a high LNC estimation accuracy with a root mean square error of only 0.59 g∕kg and an R 2 of 0.71. The proposed method is capable of effectively obtaining LNC in the whole orchard with limited samples, thus providing support for the scientific management of citrus planting.

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Estimation of citrus leaves’ nitrogen content by multispectral unmanned aerial vehicle remote sensing based on semi-supervised twin neural network regression

Li,

Du,

Zhang

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

García,

Veneros,

Oliva-Cruz

et al. 2024

Atmosphere

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The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices.

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Aplicación de índices vegetales (banda roja e infrarrojo cercano) en plantaciones de aguacate

Cited by 2 publications

References 15 publications

Estimation of citrus leaves’ nitrogen content by multispectral unmanned aerial vehicle remote sensing based on semi-supervised twin neural network regression

Estimation of citrus leaves’ nitrogen content by multispectral unmanned aerial vehicle remote sensing based on semi-supervised twin neural network regression

Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform

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