In-Field Wheat Reflectance: How to Reach the Organ Scale?

Dandrifosse, Sébastien; Carlier, Alexis; Dumont, Benjamin; Mercatoris, Benoît

doi:10.3390/s22093342

Cited by 6 publications

(10 citation statements)

References 57 publications

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“…Such changes are highly likely to be genotype-specific, thus introducing a large bias in canopy-level signals if disregarded (Anderegg et al, 2020). In the future, merging different types of sensor data to extract component-level information beyond the visible spectral domain may offer significant potential for more precise and objective measurements (Dandrifosse et al, 2022a; Jagadish et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the contribution of different components of a measured scene such as soil background or different organs (leaves, stems, and ears) change dynamically over time in a genotype-dependent manner, introducing significant bias even when only relative signal changes over time are analyzed (Anderegg et al, 2020). High-resolution image data with a pixel-resolution in the sub-millimeter range facilitates the extraction of organ-level signals as well as an elimination of background signals (e.g., Dandrifosse et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

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Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies

Anderegg

Zenkl

Walter

et al. 2023

Preprint

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Maintenance of sufficient healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semi-synthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time-series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (i) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (ii) investigation of the elusive interaction between biotic stress and physiological senescence. The potentially high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies

Anderegg

Zenkl

Walter

et al. 2023

Preprint

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“…This preliminary study provided a promising method for AGB and GY monitoring, but there were some limitations. Considering that the spikes started to grow in the late reproductive stage and the main body of the plant structure gradually changed, the vegetation index lost its sensitivity at this stage, and it was also prone to saturation under the high density and high N treatment (Dandrifosse et al, 2022; Dang et al, 2019; Li et al, 2021; Xu et al, 2022). This study can improve this saturation phenomenon, and the comprehensive utilization of color, texture, and temperature eigenvalues based on source and sink performs better in estimating wheat AGB and GY under different nitrogen application rates.…”

Section: Discussionmentioning

confidence: 99%

“…For example, compared with pendulous leaves, rice stems in the straight position have less chlorophyll concentration but more weight in the same volume content (Li et al, 2021). Furthermore, the properties of crop organs are distinct to plant species and have dynamical variations in the growth stages (Dandrifosse et al, 2022). In the vegetative growth stage, wheat is mainly composed of stems and leaves.…”

Section: Introductionmentioning

confidence: 99%

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

Wang

Zhu

et al. 2022

Food and Energy Security

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Timely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source–sink‐level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High‐quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using proximal sensing technology over a 2‐year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The source dataset and sink dataset performed differently on AGB and GY estimation, but the combined source–sink dataset performed best for estimating both AGB and GY. Based on the source–sink dataset, the LASSO‐RF model was the best combination for predicting AGB and GY, with the coefficient of determination (R2) of 0.85 and 0.86, root mean square error (RMSE) of 1179.09 and 609.61 kg ha−1, and the ratio of performance to deviation (RPD) of 2.10 and 2.45, respectively. This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages.

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“…(2021) employs a b-spline approach to achieve pixel-wise alignment. The second step involved correcting the multispectral images for different light conditions during acquisition, using the method described by Dandrifosse et al. (2022) .…”

Section: Methodsmentioning

confidence: 99%

Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing

Carlier,

Dandrifosse,

Dumont

et al. 2023

Front. Plant Sci.

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Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.

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In-Field Wheat Reflectance: How to Reach the Organ Scale?

Cited by 6 publications

References 57 publications

Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies

Combining high-resolution imaging, deep learning, and dynamic modelling to separate disease and senescence in wheat canopies

Estimation of grain yield in wheat using source–sink datasets derived from RGB and thermal infrared imaging

Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing

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