Comparative leaf area index estimation using multispectral and RGB images from a UAV platform

Martínez, Jorge Martínez; Swaef, Tom De; Borra‐Serrano, Irene; Lootens, Peter; Barrero, Oscar; Fernandez-Gallego, José A.

doi:10.1117/12.2663287

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…GVI [34] The GVI index in Equation ( 8) reduces the impact of the background soil while highlighting the presence of green vegetation. In order to create new modified bands, it employs global coefficients that balance the pixel values.…”

Section: Index Definition Equationmentioning

confidence: 99%

Optimized Deep Learning Model for Flood Detection Using Satellite Images

Stateczny,

Praveena,

Krishnappa

et al. 2023

Remote Sensing

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The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.

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Section: Index Definition Equationmentioning

confidence: 99%

Optimized Deep Learning Model for Flood Detection Using Satellite Images

Stateczny,

Praveena,

Krishnappa

et al. 2023

Remote Sensing

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Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency Cherry Orchards: A Comparative Study

Morgan,

Stevenson

2024

Drones

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With the global population on the rise and arable land diminishing, the need for sustainable and precision agriculture has become increasingly important. This study explores the application of unoccupied aerial systems (UAS) in precision agriculture, specifically focusing on Montmorency cherry orchards in Payson, Utah. Despite the widespread use of UAS for various crops, there is a notable gap in research concerning cherry orchards, which present unique challenges due to their physical structure. UAS data were gathered using an RTK-enabled DJI Mavic 3M, equipped with both RGB and multispectral cameras, to capture high-resolution imagery. This research investigates two primary applications of UAS in cherry orchards: tree height mapping and crop health assessment. We also evaluate the accuracy of tree height measurements derived from three UAS data processing software packages: Pix4D, Drone2Map, and DroneDeploy. Our results indicated that DroneDeploy provided the closest relationship to ground truth data with an R2 of 0.61 and an RMSE of 31.83 cm, while Pix4D showed the lowest accuracy. Furthermore, we examined the efficacy of RGB-based vegetation indices in predicting leaf area index (LAI), a key indicator of crop health, in the absence of more expensive multispectral sensors. Twelve RGB-based indices were tested for their correlation with LAI, with the IKAW index showing the strongest correlation (R = 0.36). However, the overall explanatory power of these indices was limited, with an R2 of 0.135 in the best-fitting model. Despite the promising results for tree height estimation, the correlation between RGB-based indices and LAI was underwhelming, suggesting the need for further research.

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Comparative leaf area index estimation using multispectral and RGB images from a UAV platform

Cited by 2 publications

References 35 publications

Optimized Deep Learning Model for Flood Detection Using Satellite Images

Optimized Deep Learning Model for Flood Detection Using Satellite Images

Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency Cherry Orchards: A Comparative Study

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