Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize

Lu, Jun; Cheng, Dongling; Geng, Chenming; Zhang, Zhitao; Xiang, Youzhen; Hu, Tiantian

doi:10.1016/j.biosystemseng.2020.11.010

Cited by 89 publications

(39 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This could be due to nitrogen application at the right moment, enhancing N availability to the plant. This result is consistent with that of Lu et al (2021), who found that the rate and timing of nitrogen treatment substantially impacted maize plant height. Late nitrogen fertilization during crop flowering may be important to optimize maize agronomic performance (Lago et al 2021;Redondo-Gómez et al 2021;Wang et al 2021).…”

Section: Plant Heightsupporting

confidence: 91%

Determination of Appropriate Time of Nitrogen Fertilizer Application for Maize in the Central Highlands of Ethiopia

Mamo

Chalchissa²,

Biratu³

2022

Agrotechnology Res J

View full text Add to dashboard Cite

Excessive nitrogen fertilization and improper management can cause a decrease in NUE in the maize cropping system. Most nitrogen fertilizers are applied when the corn is 4-5 weeks after planting. However, recent studies have shown that modern hybrids take up high amounts of nitrogen at the flowering stage. This suggests that a nitrogen fertilization strategy that starts at the beginning of vegetative growth and later in the flowering phase is needed to maximize the yield of hybrid maize in upland. The study was conducted in 2018-2019 in Liban, Jawi, and Toke Kutaye Districts in the West Showa Zone of the Central Highlands of Ethiopia. The research design used a randomized block design with the one-time treatment of fertilizer application consisting of six levels, namely 1/3 at planting + 1/3 at 4-5 weeks after an emergency (WAE) + 1/3 at 70-80 days after planting (DAP ); 1/3 part at planting + 2/3 part at 4-5 WAE; 2/3 share at 4-5 WAE + 1/3 at 70-80 DAP; 1/4 at planting + 1/2 at 4-5 WAE + 1/4 at 70-80 DAP; 1/2 at 4-5 WAE + 1/2 at 70-80 DAP; and full at 4-5 WAE). Nitrogen fertilization on2/3part at 4-5 WAE + 1/3 part at 70-80 DAP significantly affected plant height, cob length, grain yield, and biomass yield, but it did not influence root and stem lodging. When the data were combined over the two years, the treatment of 2/3part N application at 4-5 WAE (knee height) + 1/3 part at 70-80 DAP (before tasseling) resulted in the highest grain yield with a yield advantage of 1,598 kg/ha and gave maximum net benefit over the typically used full application at 4-5 WAE (knee height). This fertilizer management strategy could be advised for the Liban Jawi, Toke Kutaye areas, and other similar agro-ecosystem environments.

show abstract

Section: Plant Heightsupporting

confidence: 91%

Determination of Appropriate Time of Nitrogen Fertilizer Application for Maize in the Central Highlands of Ethiopia

Mamo

Chalchissa²,

Biratu³

2022

Agrotechnology Res J

View full text Add to dashboard Cite

show abstract

“…The four-stage model was constructed based on the GBNDVI, TCARI, NRI, and MSAVI2 spectral indices, whose functions were logarithmic, linear, linear, and power for each spectral index, respectively. The form of model function is consistent with other research results [29,34,72,73]. To further analyze these indices, the sensitivity of the model using NE was discussed (Figure 6).…”

Section: Sensitivity Validity and Applicability Of The Modelsupporting

confidence: 83%

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

View full text Add to dashboard Cite

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.

show abstract

“…UAV-based hyperspectral data with plenty of shallow bands had a good performance for evaluating LNC in the different growth stages of wheat [12]. The combined features of vegetation coverage and VIs extracted from UAV-based RGB images could show some potential for estimating LNC in maize [13]. In addition, UAVbased images from RGB, multispectral, and thermal sensors have been used to acquire vegetation and temperature parameters for assessing nitrogen use efficiency in crops [14].…”

Section: Introductionmentioning

confidence: 99%

Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

Fan

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI =VI/1+FVcover, where VI denotes the reference vegetation index and FVcover refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and FVcover was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best R2 value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.

show abstract

Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize

Cited by 89 publications

References 48 publications

Determination of Appropriate Time of Nitrogen Fertilizer Application for Maize in the Central Highlands of Ethiopia

Determination of Appropriate Time of Nitrogen Fertilizer Application for Maize in the Central Highlands of Ethiopia

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

Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV

Contact Info

Product

Resources

About