In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

Huang, Shanyu; Miao, Yuxin; Yuan, Fei; Cao, Qiang; Ye, Huichun; Lenz-Wiedemann, Victoria I. S.; Bareth, Georg

doi:10.3390/rs11161847

Cited by 31 publications

(27 citation statements)

References 70 publications

(113 reference statements)

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“…The physical and chemical substances, leaf tissue, and canopy structure change as the potato grows. Consequently, the spectral characteristics reflect the changes in physiological and biochemical composition and canopy structure of crops [ 53 ]. Therefore, the reflectance spectra data can be applied to classify the potato crop growth stages.…”

Section: Discussionmentioning

confidence: 99%

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Liu

Zhao

Qiao

et al. 2020

Sensors

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Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

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Section: Discussionmentioning

confidence: 99%

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Liu

Zhao

Qiao

et al. 2020

Sensors

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“…Moreover, strong relationships between fluorescence indices and N indicators of rice at different growth stages were revealed by Huang et al [45]. To overcome the influence of growth stage or other factors, N sufficiency index (NSI) or response index (RI) is generally calculated by using a well-fertilized area as the reference to diagnose crop N status [41,45,46], but such approach will require a well-fertilized area or N rich plot, and generally cannot improve the prediction of PNC across growth stages [45]. Therefore, more studies are still needed to develop methods for reliable prediction of PNC across growth stages.…”

Section: Introductionmentioning

confidence: 95%

“…A hand-held canopy fluorescence sensor Multiplex has been used to detect N status in early growth stages of maize and it was proven that the Multiplex parameters were strongly influenced by N dose [20,44]. Moreover, strong relationships between fluorescence indices and N indicators of rice at different growth stages were revealed by Huang et al [45]. To overcome the influence of growth stage or other factors, N sufficiency index (NSI) or response index (RI) is generally calculated by using a well-fertilized area as the reference to diagnose crop N status [41,45,46], but such approach will require a well-fertilized area or N rich plot, and generally cannot improve the prediction of PNC across growth stages [45].…”

Section: Introductionmentioning

confidence: 99%

Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

et al. 2020

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Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

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“…Remote sensing of agricultural fields is important to assist its management through a low-cost and non-destructive approach. The usage of remote sensing systems supports data acquisition in a more frequent and faster manner, being more valuable to evaluate plants than most traditional agronomic procedures [1,2]. In the nutritional analysis, different remote sensing techniques were evaluated recently [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

et al. 2019

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The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

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In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

Cited by 31 publications

References 70 publications

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

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