Mert DEDEOĞLU, Levent BAŞAYİĞİT, Murat ERİŞOĞLU

Şeker pancarı yapraklarında azot durumunun spektral diskriminant analizi ile belirlenmesi

Bu çalışmada ülkemiz için stratejik öneme sahip şekerpancarı bitkisinin yaprak %N sınıflarının belirlenmesine yönelik hiperspektral yansımalar kullanılarak bir yöntem geliştirilmesi amaçlanmıştır. Bu amaçla 3 farklı vejetasyon evresini temsil eden noksan, yeter ve fazla N içerikli Hoagland sölüsyonları (Hoagland ve Arnon, 1938) ile 72 deneme bitkisi kontrollü sera şartlarında, perlit ortamında yetiştirilmiş, spektroradyometre ile 400-1000 nm arası spektral ölçümler ve %N tayini için yaprak örneklemeleri yapılmıştır. Şekerpancarı yapraklarında farklı dönem ve dozlarla ilişkili dalgaboylarının belirlenmesinde stepwise çoklu regresyon analizi uygulanmış ve belirlenen 48 farklı dalgaboyu yansıma değerinden temel bileşenler analizi ile toplam varyansa en yüksek katkıyı sağlayan 5 dalgaboyu (474-517-652-721-961 nm) model için seçilmiştir. Belirlenen dalgaboyları kullanılarak kodlanan Karesel Diskriminant Analiz (KDA) modeli 72 bitkiyi %92 doğrulukla gerçek sınıflarına (NNoksan ; %92, NYeter; %88 ve NFazla; %96) atamıştır. Modelin validasyonu için kullanılan 36 test verisinin %89 doğrulukla %N sınıflarına (NNoksan; %91, NYeter; %85 ve NFazla; %92) ayrımı yapılmış ve seçilen dalgaboylarından olan spektral yansımaların KDA modeli ile farkı vejetasyon dönemleri için şekerpancarı azotlu gübreleme ihtiyacının tespitinde kullanılabilir olduğu belirlenmiştir. Araştırma sonucu spektral veriler ile bitki besin durumunun belirlenmesine yönelik çalışmalara diskriminant modellerinin kullanımı için umut verici bulgular elde edilmiş ve KDA modelinin farklı bitki türü ve besin elementleri için kurgulanacak deneme desenlerinde kullanılarak geliştirilmesi önerilmiştir. Anahtar Kelimeler: Azot, hiperspektral yansıma, karesel diskriminant, spektroradyometre.

Anahtar Kelimeler:

Azot, hiperspektral yansıma, karesel diskriminant, spektroradyometre

Determination of sugar beet nitrogen status by spectral discriminant analysis

In this study, it is aimed to develop a method by using hyperspectral reflections to determine the leaf N% status of sugar beet which is strategically important for our country. For this purpose, 72 experimental plants were grown in controlled greenhouse conditions and perlite environment with Hoagland solutions with deficient and excess N content representing 3 different vegetation stages, then spectral measurements were taken between 400-1000 nm by spectroradiometer and leaf samples were collected for determination of N%. Stepwise multiple regression analysis was applied to determine the wavelengths associated with different periods and N doses in sugar beet leaves and 5 wavelengths (474-517-652-721-961 nm) were selected for the highest contribution to the total variance from the 48 different wavelength reflection values. The Quadratic Discriminant Analysis (QDA) model, which was coded using determined wavelengths, assigned 72 plants to their real classes (NDeficient; 92%, NSufficient; 88% and NExcess; 96%) with 92% accuracy. The 36 test data used for validation of the model were discriminated into 89% N classes (NDeficient; 91%, NSufficient; 85% and NExcess; 92%) with 89% accuracy, and it was determined that using QDA model with spectral reflections of the selected wavelengths can be used to detect for sugar beet N demand during different vegetation stages. As a result of the research has been obtained encouraging findings for the use of discriminant models to the studies on determination of plant nutritional status by spectral data and we was proposed that the QDA model should be developed using different plant species and nutrients on the experimental designs.

Keywords:

Hyperspectral reflectance, nitrogen, quadratic discriminant, spectroradiometer,

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