Early Diagnosis and Management of Nitrogen Deficiency in Plants Utilizing Raman Spectroscopy

Huang, Chung Hao; Singh, Gajendra; Park, Sang Hyoung; Chua, Nam Hai; Ram, Rajeev J.; Park, Bong Soo

doi:10.3389/fpls.2020.00663

Cited by 49 publications

(44 citation statements)

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“…An example using mechanistic knowledge for diagnostic purposes is the use of time-resolved chlorophyll a fluorescence as a unique tool to monitor bioactive P in plants and to detect latent P deficiency (Frydenvang et al, 2015;Carstensen et al, 2018Carstensen et al, , 2019. Early detection of nitrogen status in plants using Raman spectroscopy was recently reported by Huang et al (2020). Further developments within these areas may pave the road for a future where crops can be fertilized according to their physiological demand rather than based on the classical practice, where bulk fertilization often leads to application of either too little or too much, with negative impacts on both environment, climate and the economy of farmers.…”

Section: Discussionmentioning

confidence: 99%

The molecular–physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants

et al. 2020

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Summary The visual deficiency symptoms developing on plants constitute the ultimate manifestation of suboptimal nutrient supply. In classical plant nutrition, these symptoms have been extensively used as a tool to characterise the nutritional status of plants and to optimise fertilisation. Here we expand this concept by bridging the typical deficiency symptoms for each of the six essential macronutrients to their molecular and physiological functionalities in higher plants. We focus on the most recent insights obtained during the last decade, which now allow us to better understand the links between symptom and function for each element. A deep understanding of the mechanisms underlying the visual deficiency symptoms enables us to thoroughly understand how plants react to nutrient limitations and how these disturbances may affect the productivity and biodiversity of terrestrial ecosystems. A proper interpretation of visual deficiency symptoms will support the potential for sustainable crop intensification through the development of new technologies that facilitate automatised management practices based on imaging technologies, remote sensing and in‐field sensors, thereby providing the basis for timely application of nutrients via smart and more efficient fertilisation.

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Section: Discussionmentioning

confidence: 99%

The molecular–physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants

et al. 2020

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“…The 4 These authors contributed equally: Shilpi Gupta and Chung Hao Huang. * email: chua@ mail.rockefeller.edu; rajeev@mit.edu leaf-clip format for optical sensing was first introduced by Inada 33,39 and transmission based leaf-clip sensors have been commercialized by several vendors 34,35 . These leaf-clip sensors for chlorophyll are affected by leaf anatomy, cuticle reflectance, leaf veins, and the flatness of leaves 36,37 .…”

Section: Precision Agriculture Requires New Technologies For Rapid DImentioning

confidence: 99%

“…Reference Raman measurements were performed in the laboratory and spectra were acquired using Kymera 328i spectrograph (Andor, UK) employing a 600 g mm −1 optical grating and 830 nm excitation. We measured the Raman spectra of leaf sections placed on a 100 µm thick fused silica slide, from two locations per leaf (one on each side of the midvein) from the entire cross-section of a leaf 39 . In Ref.…”

Section: Instrumentation For Portable Raman Leaf-clip the Leaf-clip mentioning

confidence: 99%

“…In Ref. 39 , nitrate measurements using this benchtop Raman instrument were validated against chemical analysis of nitrate 40 for several plant species. Further validation was acquired by measurements of wild-type Arabidopsis thaliana as well as mutants affected in metabolic pathways associated with nitrogen uptake.…”

Section: Instrumentation For Portable Raman Leaf-clip the Leaf-clip mentioning

confidence: 99%

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Portable Raman leaf-clip sensor for rapid detection of plant stress

Gupta

Huang

Singh

et al. 2020

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Precision agriculture requires new technologies for rapid diagnosis of plant stresses, such as nutrient deficiency and drought, before the onset of visible symptoms and subsequent yield loss. Here, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates. We use the leaf-clip Raman sensor for early diagnosis of nitrogen deficiency of the model plant Arabidopsis thaliana as well as two important vegetable crops, Pak Choi (Brassica rapa chinensis) and Choy Sum (Brassica rapa var. parachinensis). In vivo measurements using the portable leaf-clip Raman sensor under full-light growth conditions were consistent with those obtained with a benchtop Raman spectrometer measurements on leaf-sections under laboratory conditions. The portable leaf-clip Raman sensor offers farmers and plant scientists a new precision agriculture tool for early diagnosis and real-time monitoring of plant stresses in field conditions.

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“…As discussed above, young leaves with less chlorophyll may exhibit a spectral response similar to leaves with low N concentration. In addition, many other stresses or diseases can lead to chlorophyll degradation in leaves, resulting in spectral patterns similar to leaves with low N concentration [61]. For instance, leaves with water stress in grapevine may exhibit similar spectral characteristics to leaves suffering from N deficiency as they tend to have a lower reflectivity in near-infrared and higher reflectivity in red edge and red bands compared to well-irrigated leaves [62].…”

Section: Limitation Of Multispectral Imagingmentioning

confidence: 99%

A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery

et al. 2020

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Assessment of the nitrogen status of grapevines with high spatial, temporal resolution offers benefits in fertilizer use efficiency, crop yield and quality, and vineyard uniformity. The primary objective of this study was to develop a robust predictive model for grapevine nitrogen estimation at bloom stage using high-resolution multispectral images captured by an unmanned aerial vehicle (UAV). Aerial imagery and leaf tissue sampling were conducted from 150 grapevines subjected to five rates of nitrogen applications. Subsequent to appropriate pre-processing steps, pixels representing the canopy were segmented from the background per each vine. First, we defined a binary classification problem using pixels of three vines with the minimum (low-N class) and two vines with the maximum (high-N class) nitrogen concentration. Following optimized hyperparameters configuration, we trained five machine learning classifiers, including support vector machine (SVM), random forest, XGBoost, quadratic discriminant analysis (QDA), and deep neural network (DNN) with fully-connected layers. Among the classifiers, SVM offered the highest F1-score (82.24%) on the test dataset at the cost of a very long training time compared to the other classifiers. Alternatively, QDA and XGBoost required the minimum training time with promising F1-score of 80.85% and 80.27%, respectively. Second, we transformed the classification into a regression problem by averaging the posterior probability of high-N class for all pixels within each of 150 vines. XGBoost exhibited a slightly larger coefficient of determination (R2 = 0.56) and lower root mean square error (RMSE) (0.23%) compared to other learning methods in the prediction of nitrogen concentration of all vines. The proposed approach provides values in (i) leveraging high-resolution imagery, (ii) investigating spatial distribution of nitrogen across a vine’s canopy, and (iii) defining spatial zones for nitrogen application and smart sampling.

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Early Diagnosis and Management of Nitrogen Deficiency in Plants Utilizing Raman Spectroscopy

Cited by 49 publications

References 30 publications

The molecular–physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants

The molecular–physiological functions of mineral macronutrients and their consequences for deficiency symptoms in plants

Portable Raman leaf-clip sensor for rapid detection of plant stress

A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery

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