Classification of Some Biochemical Properties with J48 Classification Tree Algorithms in Hyperspectral Data

Year 2022, Volume: 5 Issue: 2, 20 - 28, 25.12.2022

Sinan Demir , Levent Başayiğit

Abstract

Hyperspectral sensing methods have been used in agriculture for many years to determine physiological events against abiotic and biotic stress factors of plants. Studies on the determination of wavelengths associated with biochemical properties using hyperspectral data of plants in the visible and near-infrared region under sustainable agricultural production models are limited. In this research, Isparta Oil rose with the geographical indication product from organic farming medicine and aromatic plants from sustainable production models were used considering that the effect of cultural practices from abiotic and biotic stress factors is minimal. Hyperspectral measurements and feature selection of chlorophyll a (mg g-1), chlorophyll b (mg g-1), chlorophyll a+b (mg g-1), total flavonoid content (mg catechin g-1), total phenolic content (mg GAE g-1), and total antioxidant capacity (mg TEAC g-1) biochemical properties are made using the J48 classification tree algorithm in organically grown Isparta Oil Rose leaves. In the classification algorithm, different hyperspectral data (bands) are used as independent variables for each biochemical feature, while the amounts of each biochemical feature of the dependent variable are lower and higher than the mean, and the binary response variable (binary) is taken into the model. In the selection of independent variables, the correlation-based CfsSubsetEval algorithm, which does not cause multicollinearity, was used. The areas under the classification accuracies, sensitivity, specificity, and receiver operating characteristic (ROC) curves, which are the classification performances of chlorophyll a, chlorophyll b, chlorophyll a+b, total flavonoid content, total phenolic substance content, and total antioxidant capacity, are given as “72.1%, 73.3%, 76.25%, 69.6%, 71.7%, 67.5%”, “0.353, 0.440, 0.553, 0.714, 0.771, 0.657”, “0.782, 0.811, 0.802, 0.653, 0.621, 0.682” and “0.558, 0.643, 0.631, 0.638, 0.723, 0.625” were determined respectively. As a result of the study, it was concluded that the chlorophyll a+b (mg g-1) content was determined with the J48 classification tree algorithm with the highest accuracy in the classification of the biochemical contents of made the organic farming Isparta Oil rose leaves with visible and near-infrared hyperspectral data.

Keywords

Hyperspectral Sensing, Biochemical Feature, Organic Farming, Cfssubseteval, J48 Decision Tree Algorithm, Classification

Supporting Institution

TUBITAK

Project Number

TOVAG 120 O 166

Hiperspektral Verilerde J48 Sınıflandırma Ağacı Algoritmaları ile Bazı Biyokimyasal Özelliklerin Sınıflandırılması

Abstract

Hiperspektral algılama yöntemleri tarımda uzun yıllar bitkilerin abiyotik ve biyotik stres faktörlerine karşı fizyolojik olayların belirlenmesinde kullanılmaktadır. Sürdürülebilir tarımsal üretim modelleri altındaki bitkilerin görünür ve yakın kızılötesi bölgedeki hiperspektral verileri kullanılarak biyokimyasal özellikler ile ilişkili dalga boylarının belirlenmesi üzerine araştırmalar sınırlı sayıdadır. Bu araştırmada, sürdürülebilir üretim modellerinden organik tarım yapılan tıbbı ve aromatik bitkilerden coğrafi işaretli Isparta Yağ gülü abiyotik ve biyotik stres faktörlerinden kültürel uygulamalardan kaynaklı etkinin en az olması göz önüne alınarak kullanılmıştır. Organik tarım yapılan Isparta Yağ gülü yapraklarında hiperspektral ölçümler ve klorofil a (mg g-1), klorofil b (mg g-1), klorofil a+b (mg g-1), toplam flavonoid madde içeriği (mg catechin g-1), toplam fenolik madde içeriği (mg GAE g-1) ve toplam antioksidan kapasitesi (mg TEAC g-1) biyokimyasal özelliklerin J48 sınıflandırma ağacı algoritması kullanılarak öznitelik seçimi yapılmıştır. Sınıflandırma algoritmasında her bir biyokimyasal özellik için bağımsız değişken olarak farklı hiperspektral veriler (bantlar) kullanırken, bağımlı değişken her bir biyokimyasal özelliklerinin miktarlarının ortalamaya göre düşük ve yüksek olması ikili yanıt değişkeni (binary) şeklinde modele alınmıştır. Bağımsız değişken seçiminde ise çoklu doğrusal bağlantıya (multicollinearity) neden olmayan korelasyon tabanlı CfsSubsetEval algoritması kullanılmıştır. Klorofil a, klorofil b, klorofil a+b, toplam flavonoid madde içeriği, toplam fenolik madde içeriği ve toplam antioksidan kapasitesi özelliklerinin sınıflandırma performansları olan sınıflama doğrulukları, duyarlılık, özgüllük, alıcı işletim karakteristiği (ROC) eğrileri altında kalan alanlar sırasıyla, “%72.1, %73.3, %76.25, %69.6, %71.7, %67.5”, “0.353, 0.440, 0.553, 0.714, 0.771, 0.657”, “0.782, 0.811, 0.802, 0.653, 0.621, 0.682” ve “0.558, 0.643, 0.631, 0.638, 0.723, 0.625” olduğu belirlenmiştir. Çalışma sonucunda organik tarım yapılan Isparta Yağ gülü yapraklarının biyokimyasal içeriklerinin görünür ve yakın kızılötesi hiperspektral veriler ile sınıflamasında klorofil a+b (mg g-1) içeriğinin J48 sınıflandırma ağacı algoritması ile en yüksek doğrulukta belirlendiği sonucuna varılmıştır.

Keywords

Hiperspektral algılama, biyokimyasal özellik, organik tarım, CfsSubsetEval, J48 karar ağacı algoritması, sınıflama

Project Number

TOVAG 120 O 166

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