Soybean varieties discrimination using non-imaging hyperspectral sensor

Silva, Carlos Antônio da; Nanni, Marcos Rafael; Shakir, Muhammad; Teodoro, Paulo Eduardo; Oliveira‐Júnior, José Francisco de; Cézar, Everson; Góis, Givanildo de; Lima, Mendelson; Wojciechowski, Júlio César; Shiratsuchi, L. S.

doi:10.1016/j.infrared.2018.01.027

Cited by 51 publications

(23 citation statements)

References 28 publications

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“…Within the few experiments conducted on variety discrimination, Silva et al [13] reported the effectiveness of the GNDVI index in terms of cultivar discrimination for soybean, with a smaller contribution emerging for some of the indices that resulted as not very informative in our experiment (OSAVI, EVI2, TVI, and SAVI). Kyratzis et al [6] observed statistically significant differences between a set of 20 durum wheat variety genotypes for VIs (NDVI, SR, and GNDVI).…”

Section: Discussionmentioning

confidence: 52%

“…Anyway, individual bands often result as being less sensitive to vegetation parameters, whereas their combination, known as vegetation indices (VIs), can functionally relate crop characteristics and spectral reflectance [12]. Silva Junior et al [13] were able to discriminate different soybean cultivars through multivariate analysis of different VIs. Caturegli et al [14], in a study focused on 20 turfgrass species/cultivars, measured crop reflectance in the visible and near-infrared spectra, calculating 15 different VIs that could discriminate between different cultivars within species.…”

Section: Introductionmentioning

confidence: 99%

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Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

et al. 2019

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The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green520–600, red630–690 and near-infrared760–900 bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis—PCA and linear discriminant analysis—LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases.

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Section: Discussionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

et al. 2019

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“…Recently, geotechnology has been gaining adherents in Brazil, especially in sugarcane fields [30,31], and later in grain fields, such as soybean [32]. Although precision tools currently are more used for planting/sowing operations, fertilization and weed control, there is a growing interest of researchers in providing tools to be used in insect and plant pathogen (disease) management [33].…”

Section: Resultsmentioning

confidence: 99%

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

Barros¹,

Schutze²,

Filho³

et al. 2020

Preprint

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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.

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“…Gutierrez et al [87] and Gizaw et al [88] highlighted the potential of multispectral radiometers to detect differences between wheat genotypes. Silva Júnior et al [89] used a non-imaging hyperspectral sensor to discriminate four soybean varieties through their spectral profile. Good results have been achieved for discriminating cultivars in permanent crops.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

et al. 2020

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Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are affecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil differentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be used to map them. Additional advantages of olive varieties mapping were further discussed.

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Soybean varieties discrimination using non-imaging hyperspectral sensor

Cited by 51 publications

References 28 publications

Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

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

Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

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