Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing
Remote sensing can be applied to optimize efficiency in pest detection, as an insect sampling tool. This efficiency can result in more precise recommendations for decision making in pest management. Pest detection with remote sensing is often feasible because plant biotic stress caused by herbivory...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/22/3828 |
| _version_ | 1797547225841139712 |
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| author | José Pinto Scott Powell Robert Peterson David Rosalen Odair Fernandes |
| author_facet | José Pinto Scott Powell Robert Peterson David Rosalen Odair Fernandes |
| author_sort | José Pinto |
| collection | DOAJ |
| description | Remote sensing can be applied to optimize efficiency in pest detection, as an insect sampling tool. This efficiency can result in more precise recommendations for decision making in pest management. Pest detection with remote sensing is often feasible because plant biotic stress caused by herbivory triggers a defensive physiological response in plants, which generally results in changes to leaf reflectance. Therefore, the key objective of this study was to use hyperspectral proximal remote sensing and gas exchange parameters to characterize peanut leaf responses to herbivory by <i>Stegasta bosqueella</i> (Lepidoptera: Gelechiidae) and <i>Spodoptera cosmioides</i> (Lepidoptera: Noctuidae), two major pests in South American peanut (<i>Arachis hypogaea</i>) production. The experiment was conducted in a randomized complete block design with a 2 × 3 factorial scheme (two lepidopterous species and 3 categories of injury). The injury treatments were: (1) natural infestation by third instars of <i>S. bosqueella</i>, (2) natural infestation by third instars of <i>S. cosmioides</i>, and (3) simulation of injury with scissors to mimic larval injury. We verified that peanut leaf reflectance is different between herbivory by the two larval species, but similar among real and simulated defoliation. Similarly, we observed differences in photosynthetic rate, stomatal conductance, transpiration, and photosynthetic water use efficiency only between species but not between real and simulated larval defoliation. Our results provide information that is essential for the development of sampling and economic thresholds of <i>S. bosqueella</i> and <i>S. cosmioides</i> on the peanut. |
| first_indexed | 2024-03-10T14:40:17Z |
| format | Article |
| id | doaj.art-cc4b2fbc02684b63bd0ba7db9c4332e3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:40:17Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cc4b2fbc02684b63bd0ba7db9c4332e32023-11-20T21:51:18ZengMDPI AGRemote Sensing2072-42922020-11-011222382810.3390/rs12223828Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote SensingJosé Pinto0Scott Powell1Robert Peterson2David Rosalen3Odair Fernandes4School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Rod. Prof. Paulo Donato Castellane, km 5, 14884-900 Jaboticabal, SP, BrazilDepartment of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USADepartment of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USASchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Rod. Prof. Paulo Donato Castellane, km 5, 14884-900 Jaboticabal, SP, BrazilSchool of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Rod. Prof. Paulo Donato Castellane, km 5, 14884-900 Jaboticabal, SP, BrazilRemote sensing can be applied to optimize efficiency in pest detection, as an insect sampling tool. This efficiency can result in more precise recommendations for decision making in pest management. Pest detection with remote sensing is often feasible because plant biotic stress caused by herbivory triggers a defensive physiological response in plants, which generally results in changes to leaf reflectance. Therefore, the key objective of this study was to use hyperspectral proximal remote sensing and gas exchange parameters to characterize peanut leaf responses to herbivory by <i>Stegasta bosqueella</i> (Lepidoptera: Gelechiidae) and <i>Spodoptera cosmioides</i> (Lepidoptera: Noctuidae), two major pests in South American peanut (<i>Arachis hypogaea</i>) production. The experiment was conducted in a randomized complete block design with a 2 × 3 factorial scheme (two lepidopterous species and 3 categories of injury). The injury treatments were: (1) natural infestation by third instars of <i>S. bosqueella</i>, (2) natural infestation by third instars of <i>S. cosmioides</i>, and (3) simulation of injury with scissors to mimic larval injury. We verified that peanut leaf reflectance is different between herbivory by the two larval species, but similar among real and simulated defoliation. Similarly, we observed differences in photosynthetic rate, stomatal conductance, transpiration, and photosynthetic water use efficiency only between species but not between real and simulated larval defoliation. Our results provide information that is essential for the development of sampling and economic thresholds of <i>S. bosqueella</i> and <i>S. cosmioides</i> on the peanut.https://www.mdpi.com/2072-4292/12/22/3828remote sensinglepidopteran defoliatorherbivorybiotic stresspeanut IPMprecision pest management |
| spellingShingle | José Pinto Scott Powell Robert Peterson David Rosalen Odair Fernandes Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing Remote Sensing remote sensing lepidopteran defoliator herbivory biotic stress peanut IPM precision pest management |
| title | Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing |
| title_full | Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing |
| title_fullStr | Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing |
| title_full_unstemmed | Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing |
| title_short | Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing |
| title_sort | detection of defoliation injury in peanut with hyperspectral proximal remote sensing |
| topic | remote sensing lepidopteran defoliator herbivory biotic stress peanut IPM precision pest management |
| url | https://www.mdpi.com/2072-4292/12/22/3828 |
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