Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images

Ge, Haixiao; Xiang, Haitao; Ma, Fang; Li, Zhenwang; Qiu, Zhengchao; Tan, Zhengzheng; Du, Changwen

doi:10.3390/rs13091620

Cited by 30 publications

(25 citation statements)

References 69 publications

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“…While texture features may add additional information to FHB estimation, crop infection is more directly related to color information rather than the spatial arrangement of colors ( Li et al, 2019 ). What’s more, since color images highlight specific vegetation greenness and are considered to be less sensitive to changes in light conditions, color features extracted from RGB images have the potential to provide crop growth and nutritional status, immediately providing researchers and farmers with a realistic and intuitive visualization of crop growth status ( Du and Noguchi, 2017 ; Ge et al, 2021 ). At present, some scholars use color features to estimate the nitrogen density of winter wheat leaves ( Rorie et al, 2011 ), estimate the leaf area index of rice ( Li et al, 2019 ), monitor the growth status of wheat ( Du and Noguchi, 2017 ), and accurately detect wheat FHB at the spikes scale ( Huang et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…For the color features selection, we calculated color indices through band combinations to indicate different aspects of wheat infection ( Li et al, 2019 ; Huang et al, 2020 ; Ge et al, 2021 ). Color feature is the most widely used visual feature in image retrieval; it is usually related to the object or scene contained in the image; at the same time, color feature is less dependent on the size, orientation, and perspective of the image itself, making it highly robust ( Huang et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

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Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Zhang

Huang

et al. 2022

Front. Plant Sci.

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Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Zhang

Huang

et al. 2022

Front. Plant Sci.

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“…Color moments are used to represent the color distribution in the image (Ge et al, 2021 ). Since the color information is mainly distributed in low-order moments, first-order moments (mean, MEA), second-order moments (variance, VAR), and third-order moments (skewness, SKE) are sufficient to express the color distribution of the image.…”

Section: Methodsmentioning

confidence: 99%

“…The calculation equation is shown in Table 1, where the P(i, j) is the value of the GLCM in the ith row and jth column, k is the number of gray levels in the GLCM. The gray level is 256, the step size is 1, the angle is 0 • , 45 Color moments are used to represent the color distribution in the image (Ge et al, 2021). Since the color information is mainly distributed in low-order moments, first-order moments (mean, MEA), second-order moments (variance, VAR), and third-order moments (skewness, SKE) are sufficient to express the color distribution of the image.…”

Section: Featurementioning

confidence: 99%

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Yao

Zhang

et al. 2022

Front. Plant Sci.

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Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best “CWT spectra” model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of “CWT-9 spectra + texture,” and its determination coefficients (R2val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R2val = 0.66, RMSEval = 0.34), the R2val increased by 0.24. Different from our hypothesis, the combined feature based on “CWT spectra + color + texture” cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.

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“…UAVs are easy to carry and operate and can help realize the real-time monitoring of nutrients and water management [31], weed control, disease, and pest detection and can give an estimation of the grain yield [22,32] of different crops (such as rice [33,34], wheat [35], maize [36], etc. ), thus providing a guarantee for the SCM.…”

Section: Introductionmentioning

confidence: 99%

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

et al. 2022

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Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution.

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Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images

Cited by 30 publications

References 69 publications

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

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