2. Leaf area index estimation in maize breeding trials from RGB imagery and machine learning algorithms

Castro-Valdecantos, Pedro; Apolo-Apolo, O.E.; Pérez-Ruiz, M.; Egea, Gregorio

doi:10.3920/978-90-8686-916-9_2

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Currently, two widely used methods for LAI measurement are direct and indirect. While direct measurement through field observations boasts high accuracy, it is labor-intensive and destructive, making it impractical for large scale assessments (Castro-Valdecantos et al, 2022). Alternatively, indirect methods are based on the Beer-Lambert Law theory and typically involve optical instruments for bottom-up hemispherical photography and spectral reflectance measurements (Apolo-Apolo et al, 2020;Yan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Zu,

Yang,

Wang

et al. 2024

Front. Plant Sci.

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Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R2 = 0.83), followed by the RF model (R2 = 0.82), with the PLSR model exhibited the lowest accuracy (R2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

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Section: Introductionmentioning

confidence: 99%

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Zu,

Yang,

Wang

et al. 2024

Front. Plant Sci.

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“…There are also studies on maize LAI experiments. Castro-Valdecantos et al (2022) implemented maize breeding trials in Seville, Spain from 2018 to 2019. A total of 32 maize plots were selected, and two representative plants were randomly selected from each plot on each sampling date.…”

Section: Introductionmentioning

confidence: 99%

A dataset for estimating alfalfa leaf area and predicting leaf area index

Yang,

Ge,

Wang

et al. 2024

Front. Plant Sci.

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“…In the last decade, efforts have been made to develop methodologies for the determination of LAI in maize from optical sensors, including hyperspectral, multispectral, LiDAR and RGB sensors (Table 1). Among the methodologies employed by these authors to estimate maize LAI from images and optical data are empirical relationships with vegetation indices (Fei et al, 2012), the use of machine learning models such as deep neural networks (DNN), support vector regression (SVR) or partial least squares regression (PLSR) (Fei et al, 2012;Castro-Valdecantos et al, 2021;Liu et al, 2021), the use of look-up tables (LUT) (Fei et al, 2012;Duan et al, 2014;Richter et al, 2010;Zhao et al, 2018), or through the inversion of the PROSAIL canopy reflectance model. The coefficient of determination (R 2 ) obtained with the different methodologies for estimating LAI in maize ranged from 0.66 to 0.87 (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Leaf area index estimations by deep learning models using RGB images and data fusion in maize

et al. 2022

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The leaf area index (LAI) is a biophysical crop parameter of great interest for agronomists and plant breeders. Direct methods for measuring LAI are normally destructive, while indirect methods are either costly or require long pre- and post-processing times. In this study, a novel deep learning-based (DL) model was developed using RGB nadir-view images taken from a high-throughput plant phenotyping platform for LAI estimation of maize. The study took place in a commercial maize breeding trial during two consecutive growing seasons. Ground-truth LAI values were obtained non-destructively using an allometric relationship that was derived to calculate the leaf area of individual leaves from their main leaf dimensions (length and maximum width). Three convolutional neural network (CNN)-based DL model approaches were proposed using RGB images as input. One of the models tested is a classification model trained with a set of RGB images tagged with previously measured LAI values (classes). The second model provides LAI estimates from CNN-based linear regression and the third one uses a combination of RGB images and numerical data as input of the CNN-based model (multi-input model). The results obtained from the three approaches were compared against ground-truth data and LAI estimations from a classic indirect method based on nadir-view image analysis and gap fraction theory. All DL approaches outperformed the classic indirect method. The multi-input_model showed the least error and explained the highest proportion of the observed LAI variance. This work represents a major advance for LAI estimation in maize breeding plots as compared to previous methods, in terms of processing time and equipment costs.

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2. Leaf area index estimation in maize breeding trials from RGB imagery and machine learning algorithms

Cited by 3 publications

References 11 publications

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

A dataset for estimating alfalfa leaf area and predicting leaf area index

Leaf area index estimations by deep learning models using RGB images and data fusion in maize

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