Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer
Potato (Solanum tuberosum L.) and winter wheat (Triticum aestivum L.) fields with ongoing field trials testing use of fungicides were evaluated using two camer-as (RGB-sensors that registers light in the red (R), green (G) and blue (B) wave-length bands) and one multispectral sensor (that registers...
| Autor principal: | |
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| Formato: | L3 |
| Lenguaje: | sueco Inglés |
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SLU/Dept. of Soil and Environment
2017
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| Materias: |
| _version_ | 1855571636430831616 |
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| author | Holmberg, Martin |
| author_browse | Holmberg, Martin |
| author_facet | Holmberg, Martin |
| author_sort | Holmberg, Martin |
| collection | Epsilon Archive for Student Projects |
| description | Potato (Solanum tuberosum L.) and winter wheat (Triticum aestivum L.) fields with ongoing field trials testing use of fungicides were evaluated using two camer-as (RGB-sensors that registers light in the red (R), green (G) and blue (B) wave-length bands) and one multispectral sensor (that registers light in five narrow bands in the visible and near infrared region of the electromagnetic spectrum). Data was collected using the three sensors at multiple times throughout the season, Seven times from the 20th of June to August 17th in parcels with potatoes and five times from 8th of June to July 23rd in trials with winter wheat. The collected data were later used in creating a mosaic, from which values of reflectance was extract-ed and compared to traditional (manual) methods of estimating the severity of diseases. This was carried out throughout the season. The ambition of the present thesis was to test whether it is possible to use Unmanned Aerial Vehicle (UAV)-borne sensors to detect diseases in fields with potatoes and winter wheat. The evaluated diseases were potato late blight (Phytophtora infestans) and septoria leaf blotch (Septoria tritici). In winter wheat we were not able to find any correlation neither between single bands of reflectance nor vegetation indices (GRVI) and the amount of S.tritici in the crop (p>0.05). However, the GRVI-index was found suitable to evaluate the amount of green leaf area (p= 0.05) in the canopy. The season in which this study was carried out (2016) lacked in rain during June and July which might have contributed to the unusually low amount of fungal diseases recorded. The present thesis concludes that it is possible to detect areas with pos-sible infections due to changes in values of reflectance using multispectral sensors and its correlation to infections. It is possible to use it, together with manual field observations, as decision support for application of fungicides. However it is not certain the disease is detected early enough not to spread further into the field. |
| format | L3 |
| id | RepoSLU10093 |
| institution | Swedish University of Agricultural Sciences |
| language | swe Inglés |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher | SLU/Dept. of Soil and Environment |
| publisherStr | SLU/Dept. of Soil and Environment |
| record_format | eprints |
| spelling | RepoSLU100932017-04-11T11:15:06Z Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer Assessing plant health in potatoes and cereal crops using air-borne multispectral sensors Holmberg, Martin höstvete bladmögel svartpricksjuka UAS Solanum tuberosum Triticum aestivum Potato (Solanum tuberosum L.) and winter wheat (Triticum aestivum L.) fields with ongoing field trials testing use of fungicides were evaluated using two camer-as (RGB-sensors that registers light in the red (R), green (G) and blue (B) wave-length bands) and one multispectral sensor (that registers light in five narrow bands in the visible and near infrared region of the electromagnetic spectrum). Data was collected using the three sensors at multiple times throughout the season, Seven times from the 20th of June to August 17th in parcels with potatoes and five times from 8th of June to July 23rd in trials with winter wheat. The collected data were later used in creating a mosaic, from which values of reflectance was extract-ed and compared to traditional (manual) methods of estimating the severity of diseases. This was carried out throughout the season. The ambition of the present thesis was to test whether it is possible to use Unmanned Aerial Vehicle (UAV)-borne sensors to detect diseases in fields with potatoes and winter wheat. The evaluated diseases were potato late blight (Phytophtora infestans) and septoria leaf blotch (Septoria tritici). In winter wheat we were not able to find any correlation neither between single bands of reflectance nor vegetation indices (GRVI) and the amount of S.tritici in the crop (p>0.05). However, the GRVI-index was found suitable to evaluate the amount of green leaf area (p= 0.05) in the canopy. The season in which this study was carried out (2016) lacked in rain during June and July which might have contributed to the unusually low amount of fungal diseases recorded. The present thesis concludes that it is possible to detect areas with pos-sible infections due to changes in values of reflectance using multispectral sensors and its correlation to infections. It is possible to use it, together with manual field observations, as decision support for application of fungicides. However it is not certain the disease is detected early enough not to spread further into the field. Fält med potatis (Solanum tuberosum L.) och höstvete (Triticum aestivum L.) i vilka det pågick försök med fungicider undersöktes med två kameror (RGB-sensorer som registrerar ljus i det röda (R), gröna (G) och blå (B) våglängdsområ-det) och en multispektral sensor (som registrerar ljus i fem smala band i det syn-liga och nära infraröda våglängdsområdet). Bildmaterial samlades in vid sju till-fällen mellan 20 juni och 17 augusti för potatisen och fem tillfällen mellan 8 juni och 23 juli för höstvetet. Mosaiker bildades av de bilder som tagits, ur vilka reflek-tansvärden sedan extraherades för att jämföras med manuellt utförda graderingar av angreppsgraden i beståndet. Syftet var att se om sensortekniken var kapabel att upptäcka sjukdomar i ett tidigt stadium och fungera som underlag för beslut om växtskyddsbehandlingar alternativt användas för dokumentation och gradering av angrepp i fältförsök. I höstvete (som enbart undersöktes med RGB-sensor) kunde ingen statistiskt signifikant korrelation mellan förekomsten av svartpricksjuka (Septoria tritici) och reflektansvärden för enskilda band eller Green-Red-Vegetation Index (GRVI) ses (d.v.s. p >0.05) men tekniken visade sig istället fun-gera för att bedöma beståndets grönhet (p = 0,05), vilket också var en bestånds-egenskap som graderades för hand. Året för undersökningarna (2016) fanns det en ovanligt låg förekomst av svampsjukdomar i fält. Arbetet visar att det är fullt möj-ligt att utföra en datainsamling över fält som baserat på reflektansavvikelser kan detektera möjliga angrepp av bladmögel och efter manuell fältkontroll styra fungi-cidbehandling. Det är dock tveksamt om vi ser resultaten i tid för att hindra en fortsatt spridning av bladmögel i beståndet. SLU/Dept. of Soil and Environment 2017 L3 swe eng https://stud.epsilon.slu.se/10093/ |
| spellingShingle | höstvete bladmögel svartpricksjuka UAS Solanum tuberosum Triticum aestivum Holmberg, Martin Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| title | Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| title_full | Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| title_fullStr | Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| title_full_unstemmed | Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| title_short | Sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| title_sort | sjukdomsidentifiering i potatis och stråsäd med luftburna multispektrala sensorer |
| topic | höstvete bladmögel svartpricksjuka UAS Solanum tuberosum Triticum aestivum |