Using Passive and Active Multispectral Sensors on the Correlation With the Phenological Indices of Cotton

Souza, Heloisa Bueno; Baio, Fábio Henrique Rojo; Neves, Danilo Carvalho

doi:10.1590/1809-4430-eng.agric.v37n4p782-789/2017

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…This fact happens because the vegetative growth of this crop produces a lot of plant matter originating from the increase of branches and leaves, covering the soil and increasing VI values. Souza et al (2017) evaluated the correlation of VI to phenological indices in cotton and reported values of over 80% of similarity for plant height and over 70% for number of branches per plant. Motomiya et al (2014) observed the behavior of the interaction of growth regulator doses, nitrogen topdressing, and VI, where they presented increasing VI values at the initial stages of the cotton crop until 67 DAE.…”

Section: Correlation Between Phenological Variables and Yieldmentioning

confidence: 99%

“…Monitoring the dynamics of terrestrial vegetation using remote sensing techniques may be relevant for agricultural activities. Currently, crops have been studied mainly by the analysis of their biophysical data for agronomic parameters (SOUZA et al, 2017). Besides remote sensing techniques, the use of vegetation indices (VI) of multispectral optical sensors correlates adequately to various plant growth attributes, such as plant biomass and leaf nitrogen (PORTZ et al, 2012;AMARAL et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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In situ remote sensing as a strategy to predict cotton seed yield

et al. 2019

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Crop harvest scheduling and profits and losses predications require strategies that estimate crop yield. This work aimed to investigate the contribution of phenological variables using path analysis and remote sensing techniques on cotton boll yield and to generate a model using decision trees that help predict cotton boll yield. The sampling field was installed in Chapadão do Céu, in an area of 90 ha. The following phenological variables were evaluated at 30 sample points: plant height at 26, 39, 51, 68, 82, 107, 128, and 185 days after emergence (DAE); number of floral buds at 68, 81, 107, 128, and 185 DAE; number of bolls at 185 DAE; Rededge vegetation index at 23, 35, 53, 91, and 168 DAE; and cotton boll yield. The main variables that can be used to predict cotton boll yield are the number of floral buds (at 107 days after emergence) and the Rededge vegetation index (at 53 and 91 days after emergence). To obtain higher cotton boll yields, the Rededge vegetation index must be greater than 39 at 53 days after emergence, and the plant must present at least 14 floral buds at 107 days after emergence.

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Section: Correlation Between Phenological Variables and Yieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In situ remote sensing as a strategy to predict cotton seed yield

et al. 2019

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“…A photoelectric induction circuit receives crop canopy reflectance spectrum to calculate vegetation index. However, existing active sensors have a small detection area and require close-range detection, it is difficult to obtain large-scale macro-vegetation index information [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Illumination Compensation Technique for Multi-Spectral Imaging in NDVI Detection

Jiang

Wang

et al. 2019

Sensors

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To overcome the dependence on sunlight of multi-spectral cameras, an active light source multi-spectral imaging system was designed and a preliminary experimental study was conducted at night without solar interference. The system includes an active light source and a multi-spectral camera. The active light source consists of four integrated LED (Light Emitting Diode) arrays and adjustable constant current power supplies. The red LED arrays and the near-infrared LED arrays are each driven by an independently adjustable constant current power supply. The center wavelengths of the light source are 668 nm and 840 nm, which are consistent with that of filter lens of the Rededge-M multi-spectral camera. This paper shows that the radiation intensity measured is proportional to the drive current and is inversely proportional to the radiation distance, which is in accordance with the inverse square law of light. Taking the inverse square law of light into account, a radiation attenuation model was established based on the principle of image system and spatial geometry theory. After a verification test of the radiation attenuation model, it can be concluded that the average error between the radiation intensity obtained using this model and the actual measured value using a spectrometer is less than 0.0003 w/m2. In addition, the fitting curve of the multi-spectral image grayscale digital number (DN) and reflected radiation intensity at the 668 nm (Red light) is y = −3484230x2 + 721083x + 5558, with a determination coefficient of R2 = 0.998. The fitting curve with the 840 nm (near-infrared light) is y = 491469.88x + 3204, with a determination coefficient of R2 = 0.995, so the reflected radiation intensity on the plant canopy can be calculated according to the grayscale DN. Finally, the reflectance of red light and near-infrared light can be calculated, as well as the Normalized Difference Vegetation Index (NDVI) index. Based on the above model, four plants were placed at 2.85 m away from the active light source multi-spectral imaging system for testing. Meanwhile, NDVI index of each plant was measured by a Greenseeker hand-held crop sensor. The results show that the data from the two systems were linearly related and correlated with a coefficient of 0.995, indicating that the system in this article can effectively detect the vegetation NDVI index. If we want to use this technology for remote sensing in UAV, the radiation intensity attenuation and working distance of the light source are issues that need to be considered carefully.

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“…Cotton yield is correlated with the amount of active photosynthetic leaf tissue, which is highly related to biomass and the leaf area index, which can be estimated by the vegetation indices (VIs) from crop canopy reflectance (Muharam et al, 2014; Yang and Everitt, 2012; Yang et al, 2015). A vegetation index is a single number calculated from wavelength reflectances measured from remote sensors and relates to biomass and plant vigor (Souza et al, 2017). Among the several VIs, the normalized difference vegetation index (NDVI) is widely studied.…”

mentioning

confidence: 99%

“…The relationship between NDVI and the cotton growth parameters is dependent on the phenological stage at which the sensor readings are performed. The VIs acquisition readings are taken during the fruiting phenological period, which is the phase of the cropping cycle presenting a higher correlation with yield (Gutierrez et al, 2012; Raper and Varco, 2015; Souza et al, 2017; Zarco‐Tejada et al, 2005). By measuring NDVI variability, which provides site‐specific information on biomass production and growth stage, it is possible to improve yields by identifying areas requiring corrective action, growth regulators, or other inputs (Baio et al, 2018).…”

mentioning

confidence: 99%

Soil Chemical Attributes, Soil Type, and Rainfall Effects on Normalized Difference Vegetation Index and Cotton Fiber Yield Variability

2019

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Understanding of how crop yield is influenced by interactions between the soil and environment is needed to minimize agricultural inputs and reduce the impacts of agriculture on the environment. The objective of this study was to determine the effect of soil chemical attributes, soil type, and rainfall on cotton (Gossypium hirsutum L.) yields and yield variability. In this study, the average cotton fiber yield was collected from 101 fields over nine crop seasons. In these fields, located in central Brazil, the average and standard deviation of normalized difference vegetation index (NDVI) were determined. Growing season rainfall and soil nutrients were measured. The variables analyzed were: average cotton fiber yield, NDVI from Landsat images calculated at the boll development phenological stage (enlargement and fiber filling phases), CV of the NDVI, precipitation during the cycle, and soil chemical attributes. The average field NDVI values collected at the boll development phenological stage were correlated to yield (r = 0.50; p < 0.05). Fields with a higher CV for the NDVI values generally had lower yields. The effect of precipitation depends on the cotton phenological stage and soil type. A high rainfall volume at the beginning of the cropping cycle increased plant height variability, which harmed the cotton fiber yield. The highest average values of aluminum content, aluminum saturation, and potential acidity promoted variability in cotton plant growth. Soil chemical attributes related to aluminum were negatively correlated with yield (r = −0.34; p > 0.05). Core Ideas Annual averages of NDVI and yield presented a significant correlation. The effect of precipitation depends on the cotton phenological stage and soil type. Soil chemical variables related to aluminium are negatively correlated with the yield.

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Using Passive and Active Multispectral Sensors on the Correlation With the Phenological Indices of Cotton

Cited by 10 publications

References 14 publications

In situ remote sensing as a strategy to predict cotton seed yield

In situ remote sensing as a strategy to predict cotton seed yield

A Novel Illumination Compensation Technique for Multi-Spectral Imaging in NDVI Detection

Soil Chemical Attributes, Soil Type, and Rainfall Effects on Normalized Difference Vegetation Index and Cotton Fiber Yield Variability

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