Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress

Chen, Junxu; Liu, Fan; Wang, Rui; Fan, Yuanfang; Raza, Muhammad Ali; Liu, Qinlin; Wang, Zhong Lin; Cheng, Yajiao; Wu, Xiaoling; Yang, Feng; Yang, Wenyu

doi:10.1016/j.compag.2018.12.003

Cited by 44 publications

(28 citation statements)

References 27 publications

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“…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%

“…Regarding the monitoring of plant and leaf nutritional conditions by remote sensing systems, recent research has made significant advances, especially in the estimation of nitrogen (N) content [1][2][3][4]21,25,28,31]. These studies were conducted at orbital, aerial, terrestrial, or proximal levels in different crops.…”

Section: Introductionmentioning

confidence: 99%

“…N deficiency is linked to a characteristic chlorosis symptom, which is observable at the visible spectra [21,25,28]. Still, considerable research was also able to identify spectral bands and wavelengths in the near and short-wave infrared regions related to this nutrient [2,3,25,28,36]. Regardless, even though N is a pretty standard nutrient to be evaluated by remote sensing systems, the same cannot be said about others.…”

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…By comparing the tting models of the two relative variables, the results obtained by the three weighting methods all showed that the weighted rLAI-based models had higher accuracy than the weighted rCI red edge -based models. The optimal estimation models of potato yield can be determined as Equations (20) and (21). where yield (LAI) is the estimated yield using rLAI based on weighted growth stage.…”

Section: Estimation Of Potato Yield Based On Weighted Growth Stagementioning

confidence: 99%

“…Vegetation canopy spectrum is closely related to crop growth, especially the visible range affected by pigment and the near-infrared (NIR) bands affected by cell tissue and canopy structure [16][17]. Therefore, the vegetation index (VI) calculated by these bands has been widely used for the monitoring and estimation of vegetation characteristic parameters, such as leaf area index (LAI) [18], biomass [19], chlorophyll content [20], nitrogen content and carbon content [21], and achieved high accuracy. In addition, various VIs showed great differences when applied in diverse scenarios.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Luo

et al. 2020

Preprint

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Background The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative LAI (rLAI) data to improve the accuracy of potato yield estimation based on weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with single stage and weighted stage was completed. Results The results showed that among the six test rVIs (rNDVI, rCIred edge, rCIgreen, rEVI2, rNDRE and rMTCI), rCIred edge was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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Plant‐based protein crops and their improvement: Current status and future perspectives

Singh,

Elango,

Raigne

et al. 2024

Crop Science

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The plant‐based protein industry is rapidly growing and is projected to grow to over $27 billion by 2030. In addition to monetary benefits, plant‐based protein crops, particularly pulse crops (i.e., legume species with dry edible seed), can supplement the existing crop biodiversity and break the disease and insect pest cycle in drastically changing climatic conditions. Most commercially available plant‐based protein ingredients come from only 2% of the 150 plant species on which our food supply depends. Therefore, it is imperative to diversify the cropping system with pulse crops that provide a more nutritious, climate‐resilient, sustainable food value chain. At present, a large portion of US pulse crop production is exported, yet the shifting demographics, dietary preferences, and a rise in domestic consumption present enormous potential for existing and emerging pulse crops, as well as their breeding. Despite a rise in demand from consumers and industries, farmers are yet to fully reap the benefits of these crops due to a shortage of improved varieties. These new varieties could lead to a diverse, sustainable protein supply that satisfies the growing domestic and global demand for plant‐based foods. This review intends to comprehensively explore the geographical and cultural acceptance of plant‐based proteins, manufacturing techniques, protein products, anti‐nutritional factors, techniques and tools for protein estimation, high‐throughput estimation of seed protein, protein composition requirements in food, breeding strategies, crop improvement, and resources to improve plant‐based protein pulse crops.

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Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress

Cited by 44 publications

References 27 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

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant‐based protein crops and their improvement: Current status and future perspectives

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