Remote Detection of Water and Nutritional Status of Soybeans Using Uav-Based Images

Andrade, A. S. de; Silva, Silvestre P. da; Setubal, Ingrid Silva; Souza, Henrique Antunes de; Vieira, Paulo Fernando de Melo Jorge

doi:10.1590/1809-4430-eng.agric.v42n2e20210177/2022

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…Another typical technique is hyperspectral remote sensing, which has been used to monitor changes in leaf traits and estimate soybean leaf carbon and nitrogen content. Additionally, digital images obtained using remotely piloted aircraft have been utilized to detect and monitor abiotic stresses in soybeans using digital aerial images 9 …”

Section: Introductionmentioning

confidence: 99%

Fast and low‐cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near‐infrared devices

da Silva,

Abich,

Maurer

et al. 2023

J Sci Food Agric

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BackgroundThe current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time‐consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near‐infrared spectroscopy (NIRS) and compare the performance of conventional (1,100–2,500 nm spectral range) and portable equipment (1,100–1,700 nm spectral range). Partial least squares (PLS) regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest.ResultsThe benchtop method yielded low root mean square error of prediction (RMSEP) values for nitrogen and carbon (2.42 g kg‐1 and 4.37 g kg‐1 respectively), while the portable method had higher RMSEP values (3.82 g kg‐1 and 10.7 g kg‐1 respectively). The benchtop method did not show significant differences when compared to the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork.ConclusionThe methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost‐effective, safe analytical data aligning with the principles of green analytical chemistry.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%

Fast and low‐cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near‐infrared devices

da Silva,

Abich,

Maurer

et al. 2023

J Sci Food Agric

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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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MOISTURE LIMITS AND POTASSIUM CONCENTRATIONS IN THE SOIL SOLUTION FOR CULTIVATION OF LISIANTHUS (Eustoma grandiflorum) FERTIGATED UNDER PROTECTED ENVIRONMENT CONDITIONS

et al. 2023

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Lisianthus (Eustoma grandiflorum) has been attracting great interest due to the beauty of the colors of flowers, the firmness of stems, and postharvest durability. However, knowledge of lisianthus irrigation and fertigation has generally been based on recommendations from other countries. This study aimed to establish parameters for the rational management of lisianthus fertigation under a protected environment by determining the best lower limit of soil moisture, associated with the determination of the optimal potassium concentration to be maintained in the soil solution. The treatments consisted of five soil moisture limits for the start of irrigation and maintenance of five potassium concentrations in the soil solution, with four replications. Plots of 0.7 by 0.5 m were arranged in a completely randomized block design in a factorial scheme. The soil solution was monitored by measuring moisture as well as potassium (K + ) concentrations using a TDR100 reflectometer and solution extractors, respectively, during two cycles. The evaluated variables were the leaf area index, shoot fresh and dry matter, commercial yield, and average stem diameter. Lisianthus cultivation under the lower limit of soil moisture equal to 0.20 cm 3 cm −3 ensured the best results for all variables of commercial interest. No effect of potassium concentrations was observed on variables of commercial interest.

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Remote Detection of Water and Nutritional Status of Soybeans Using Uav-Based Images

Cited by 4 publications

References 65 publications

Fast and low‐cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near‐infrared devices

Fast and low‐cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near‐infrared devices

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

MOISTURE LIMITS AND POTASSIUM CONCENTRATIONS IN THE SOIL SOLUTION FOR CULTIVATION OF LISIANTHUS (Eustoma grandiflorum) FERTIGATED UNDER PROTECTED ENVIRONMENT CONDITIONS

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