Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing

Ji, Jiangtao; Li, Nana; Cui, Hongwei; Li, Yuchao; Zhao, Xinbo; Zhang, Haolei; Ma, Hao

doi:10.3390/agriculture13051004

Cited by 6 publications

(6 citation statements)

References 30 publications

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“…Spectral information serves as the foundational data for the optical remote sensing of spectral traits [ 46 , 47 ], and it is determined by various factors such as canopy structure and biochemical composition. Due to the uneven distribution of nutrients along the vertical scale of crops, it is often manifested on the leaves [ 29 ]. Studying the vertical variation in LNC can better elucidate changes in plant physiological processes and adjust the photosynthesis and nutrient absorption of crops, thereby optimizing agricultural management practices more effectively.…”

Section: Discussionmentioning

confidence: 99%

“…This may reduce the quality of observations for these leaves, and the spectral signals at different vertical levels may mix together, especially when the sensor resolution is relatively low. This can make it challenging to differentiate the spectral signals at different vertical levels, and in general, upper leaves usually have more sensitive spectral signals, making them easier to detect and interpret, while the signal mixing at the middle and lower layers may complicate the estimation of LNC [ 29 ]. We observed that the increase in nitrogen application led to a corresponding rise in LNC.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, for crops, there is a lack of comparison and discussion on the prediction effects of different vertical scales of LNCs. Models are often built and analyzed using LNCs from a specific site without comparison and optimization, which limits the spatial applicability of the established prediction models [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters

Sun,

Li,

Wang

et al. 2024

Plants

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Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and “three-edge” parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), “three-edge” parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters

Sun,

Li,

Wang

et al. 2024

Plants

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“…The increase in N accumulation in plants treated with HS was consistent with the increase in the SPAD index, but this relationship was significant only under adequate P supply. The SPAD index, calculated using a widely utilized chlorophyll meter in agricultural systems, guides N management via monitoring the leaf N status [ 46 ]. The humate effect on N content without a SPAD index changes under low-P conditions and can be explained via a consideration of the impact on photosynthesis.…”

Section: Discussionmentioning

confidence: 99%

Phosphorus Acquisition Efficiency and Transcriptomic Changes in Maize Plants Treated with Two Lignohumates

Santoro,

Della Lucia,

Francioso

et al. 2023

Plants

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Lignohumates are increasing in popularity in agriculture, but their chemistry and effects on plants vary based on the source and processing. The present study evaluated the ability of two humates (H1 and H2) to boost maize plant performance under different phosphorus (P) availability (25 and 250 μM) conditions in hydroponics, while understanding the underlying mechanisms. Humates differed in chemical composition, as revealed via elemental analysis, phenol and phytohormone content, and thermal and spectroscopic analyses. H1 outperformed H2 in triggering plant responses to low phosphorus by enhancing phosphatase and phytase enzymes, P acquisition efficiency, and biomass production. It contained higher levels of endogenous auxins, cytokinins, and abscisic acid, likely acting together to stimulate plant growth. H1 also improved the plant antioxidant capacity, thus potentially increasing plant resilience to external stresses. Both humates increased the nitrogen (N) content and acted as biostimulants for P and N acquisition. Consistent with the physiological and biochemical data, H1 upregulated genes involved in growth, hormone signaling and defense in all plants, and in P recycling particularly under low-P conditions. In conclusion, H1 showed promising potential for effective plant growth and nutrient utilization, especially in low-P plants, involving hormonal modulation, antioxidant enhancement, the stimulation of P uptake and P-recycling mechanisms.

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“…A smaller MSE indicates a higher alignment between predicted and original data. The final optimization yields the best regularization coefficient C and kernel function parameter S. Using these optimized values, the network is trained on the testing dataset [20]. The flowchart of the SSA-KELM algorithm is shown in Figure 3.…”

Section: Model Constructionmentioning

confidence: 99%

Monitoring Indicators for Comprehensive Growth of Summer Maize Based on UAV Remote Sensing

Ma,

Li,

et al. 2023

Agronomy

Self Cite

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Maize is one of the important grain crops grown globally, and growth will directly affect its yield and quality, so it is important to monitor maize growth efficiently and non-destructively. To facilitate the use of unmanned aerial vehicles (UAVs) for maize growth monitoring, comprehensive growth indicators for maize monitoring based on multispectral remote sensing imagery were established. First of all, multispectral image data of summer maize canopy were collected at the jointing stage, and meanwhile, leaf area index (LAI), relative chlorophyll content (SPAD), and plant height (VH) were measured. Then, the comprehensive growth monitoring indicators CGMICV and CGMICR for summer maize were constructed by the coefficient of variation method and the CRITIC weighting method. After that, the CGMICV and CGMICR prediction models were established by the partial least-squares (PLSR) and sparrow search optimization kernel extremum learning machine (SSA-KELM) using eight typical vegetation indices selected. Finally, a comparative analysis was performed using ground-truthing data, and the results show: (1) For CGMICV, the R2 and RMSE of the model built by SSA-KELM are 0.865 and 0.040, respectively. Compared to the model built by PLSR, R2 increased by 4.5%, while RMSE decreased by 0.3%. For CGMICR, the R2 and RMSE of the model built by SSA-KELM are 0.885 and 0.056, respectively. Compared to the other model, R2 increased by 4.6%, and RMSE decreased by 2.8%. (2) Compared to the models by single indicator, among the models constructed based on PLSR, the CGMICR model had the highest R2. In the models constructed based on SSA-KELM, the R2 of models by the CGMICR and CGMICV were larger than that of the models by SPAD (R2 = 0.837), while smaller than that of the models by LAI (R2 = 0.906) and models by VH (R2 = 0.902). In summary, the comprehensive growth monitoring indicators prediction model established in this paper is effective and can provide technical support for maize growth monitoring.

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Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing

Cited by 6 publications

References 30 publications

Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters

Monitoring of Nitrogen Concentration in Soybean Leaves at Multiple Spatial Vertical Scales Based on Spectral Parameters

Phosphorus Acquisition Efficiency and Transcriptomic Changes in Maize Plants Treated with Two Lignohumates

Monitoring Indicators for Comprehensive Growth of Summer Maize Based on UAV Remote Sensing

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