scholarly journals Monitoring Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) Infestation in Soybean Using Hyperspectral Remote Sensing

2020 ◽  
Author(s):  
Pedro P. S. Barros ◽  
Inana X. Schutze ◽  
Fernando H. Iost Filho ◽  
Pedro Takao Yamamoto ◽  
Peterson Fiorio ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bemisia Tabaci ◽  
Insect Pest ◽  
Remote Sensing Data ◽  
Management Program ◽  
Hyperspectral Remote Sensing ◽  
Negative Effects ◽  
Soybean Plants ◽  
Soybean Leaves ◽  
Different Levels

Although monitoring and observing insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. High infestation levels may diminish the photosynthetic activity of soybean plants, affecting their development and reducing the yield. An imprecise decision making in integrated pest management program may lead to an ineffective control in infested areas or the excessive use of insecticides. In order to reach a more efficient control of arthropods population it is important to evaluate the infestation in time to mitigate its negative effects on the crop and remote sensing is an important tool for monitoring. It was proposed that infested soybean areas could be identified, and the arthropods quantified from non-infested areas in a field by hyperspectral remote sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral remote sensing data. Therefore, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results obtained allowed to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance.

scholarly journals Monitoring Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Infestation in Soybean by Proximal Sensing

Insects ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 47
Author(s):  
Pedro P. S. Barros ◽  
Inana X. Schutze ◽  
Fernando H. Iost Filho ◽  
Pedro T. Yamamoto ◽  
Peterson R. Fiorio ◽  
...  
Keyword(s):  
Bemisia Tabaci ◽  
Photosynthetic Activity ◽  
Insect Pest ◽  
Management Program ◽  
Proximal Sensing ◽  
Soybean Plants ◽  
Soybean Leaves ◽  
Hyperspectral Sensing ◽  
Different Levels ◽  
Commercial Field

Although monitoring insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. Imprecise decision-making in an integrated pest management program may lead to ineffective control in infested areas or the excessive use of insecticides. In addition, high infestation levels may diminish the photosynthetic activity of soybean, reducing their development and yield. Therefore, we proposed that levels of infested soybean areas could be identified and classified in a field using hyperspectral proximal sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral sensing data. Therefore, cages were placed over soybean plants in a commercial field and artificial whitefly infestations were created. Later, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results allowed us to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance. In conclusion, these results show that hyperspectral sensing can potentially be used to monitor whitefly populations in soybean fields.

Natural and artificial target recognition by hyperspectral remote sensing data

2002 ◽  
Author(s):  
Bing Zhang ◽  
Liangyun Liu ◽  
Yongchao Zhao ◽  
Genxing Xu ◽  
Lanfen Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Recognition ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Late Summer ◽  
Eutrophic Lake ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Negative Effects ◽  
Eutrophic Lakes ◽  
Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

scholarly journals Characterization of Species Diversity and Forest Health using AVIRIS-NG Hyperspectral Remote Sensing Data

2019 ◽  
Vol 116 (7) ◽  
pp. 1124 ◽  
Author(s):  
C. S. Jha ◽  
, Rakesh ◽  
J. Singhal ◽  
C. S. Reddy ◽  
G. Rajashekar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Species Diversity ◽  
Forest Health ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data
Sign in / Sign up

Export Citation Format

Share Document