Assessing macro-element content in vine leaves and grape berries of
            <i>vitis vinifera</i>
            by using near-infrared spectroscopy and chemometrics

Cuq, Sébastien; Lemetter, Valérie; Kleiber, Didier

doi:10.1080/03067319.2019.1648644

Cited by 15 publications

(50 citation statements)

References 23 publications

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“…For K, a three-band combination index predicted the nutrient with an R 2 equal to 0.74 [37]. In nutrients like Mg, S, P, Ca, and others, the predictions (R 2 ) variated between lower values of 0.27 up to 0.98, depending on the method applied and the plant evaluated [24,26,30,32,38,39,50].…”

Section: Discussionmentioning

confidence: 99%

“…The usage of proximal sensors for plant evaluation has assisted phenological studies of different species. Due to the high spectral resolution capability of these sensors, studies have been relatively successful in modeling phenomena, such as the ones previously stated, but at the leaf level, like plant stress, yield prediction, nutrient content, chlorophyll, and many other attributes [24][25][26][27]. They also have the advantage of helping to define, in detail, the appropriate spectral regions to estimate these phenomena.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro- and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 × 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms’ prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

et al. 2020

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“…grapes, avocados, or mangoes), forage quality analysis, and soil analysis (Peng et al, 2015;Tang, Jones & Minasny, 2020;Ng et al, 2019). There are few published results in the literature that have explored the use of NIRS for the nutrient analysis of various plants such as mustards (Martínez-Valdivieso, Font & Río-Celestino, 2019), sorghum, oat, and corn (Savi et al, 2019), wheat and barley (Zerner & Parker, 2019), vine and grape berries (Cuq et al, 2020), and peach (Dedeoglu, 2020). The use of NIRS for the determination of plant nutrient status in cotton is limited and was reported in a study by Tarpley, Reddy & Sassenrath-Cole (2000) in the United States of America.…”

Section: Introductionmentioning

confidence: 99%

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS)

Prananto

Minasny

Weaver

2021

PeerJ

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The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350–2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R2 ≤ 0.98 for N, P, K, Ca, Mg and S) and most micronutrients (0.64 ≤ R2 ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

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“…NIRS involves the absorption of electromagnetic radiation in the 780–2500 nm wavelength range to generate spectral data ( Osborne , 2006). Using chemometrics, information can be extracted from NIR spectral data and used to create prediction models ( Cuq et al., 2020). NIRS has proven successful in predicting the N concentration of several plant species, including oilseed rape ( Liu et al., 2011), perennial ryegrass ( Gislum et al., 2004), red fescue ( Gislum et al., 2004), and pears ( Wang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Cuq et al. (2020) demonstrated the ability of NIRS (MicroNIR OnSite spectrometer; Viavi, USA) to predict leaf blade and petiole N concentration in four grape varieties in south‐western France, suggesting NIRS may be valuable tool to assess N concentration, and therefore the status of N nutrition in vines. Yet, sampling for this study was completed at veraison over one season only.…”

Section: Introductionmentioning

confidence: 99%

Predicting grapevine canopy nitrogen status using proximal sensors and near‐infrared reflectance spectroscopy

Walker

Jones

Swarts

et al. 2021

J. Plant Nutr. Soil Sci.

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Background: The current method employed by industry for tissue analysis to determine grapevine nitrogen (N) status is expensive and time intensive. Aims: This study explored the use of proximal sensors and Fourier transform near infrared spectroscopy (FT‐NIRS) to predict vine (Vitis vinifera L.) canopy N status over two growing seasons in Southern Tasmania, Australia. Methods: The GreenSeeker, Crop Circle ACS‐430 and SPAD‐502 proximal sensors were used to measure vine canopies (cv. Pinot Noir and Chardonnay) at three sampling dates (January, February, March) in the 2017/18 growing season, and two (December & February) in the 2018/19 growing season. For 12 replicates consisting of 5 vines each, a 30‐leaf sample was taken for FT‐NIRS and elemental analysis on dried, ground leaf samples. In addition, measurements with a portable FT‐NIRS were taken on fresh leaf samples (2018/19). All measurements were correlated with leaf N concentration (%) determined via elemental analysis. Results: The reliability of the proximal sensors to predict vine N content was dependent on the vine variety and sampling time. FT‐NIRS demonstrated a strong ability to predict vine N concentration independent of season, sampling time and variety. The benchtop FT‐NIRS showed the strongest predictability over both seasons (r2 = 0.94), yet the portable FT‐NIRS also showed potential (r2 = 0.76). Conclusion: Further investigation of portable FT‐NIRS technology is necessary to provide a robust model for non‐destructive vine N determination in the field.

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Assessing macro-element content in vine leaves and grape berries of vitis vinifera by using near-infrared spectroscopy and chemometrics

Cited by 15 publications

References 23 publications

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS)

Predicting grapevine canopy nitrogen status using proximal sensors and near‐infrared reflectance spectroscopy

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