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Journals / Mersin Üniversitesi Tıp Fakültesi Lokman Hekim Tıp Tarihi ve Folklorik Tıp Dergisi / 2023 / Cilt: 13 Sayı: 3

A Technological Perspective on Traditional Medicine: Classification of Plant Species with Machine Learning

Geleneksel Tıbba Teknolojik Bir Bakış: Bitki Türlerinin Makine Öğrenimi ile Sınıflandırılması

Fatma SÖĞÜT
Bora REŞİTOĞLU
Evrim Ersin KANGAL
Pages
764–774
Publication Date
2023-09-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results: It was concluded that the system identified plants with 97% accuracy.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: The aim of this study is to determine the morphological characteristics of any plant; that is, to classify it with the method of image processing and machine learning by defining it with features such as leaf shape, color or odor.
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Key Findings & Data:To verify that the system was working, 100 images of leaves and flowers were taken for each of two different plants, and the number of statistical data was increased to 700 with the ImageData Generator algorithm.
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Conclusion & Impact:Results: It was concluded that the system identified plants with 97% accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Tıbbi Bitkiler · Makine Öğrenmesi · Görüntü İşleme

Abstract

Objective: The aim of this study is to determine the morphological characteristics of any plant; that is, to classify it with the method of image processing and machine learning by defining it with features such as leaf shape, color or odor. Method: In this study, plant images obtained from an open access database called kaggle were used as a source for machine learning. After the image learning process, the leaf images of the plants were classified by the Convolutional Neural Network (CNN) method. To verify that the system was working, 100 images of leaves and flowers were taken for each of two different plants, and the number of statistical data was increased to 700 with the ImageData Generator algorithm. Results: It was concluded that the system identified plants with 97% accuracy. The performance of the machine learning algorithm can also be understood from the confusion matrix. In the method followed in this study, diagonal elements 98 and 79 of the confusion matrix were obtained. This indicates that the method we applied is statistically significant. Conclusion: Thanks to the algorithm used in this study, the identification of plants used in traditional and complementary medicine could be made with an accuracy of 97%. With this algorithm, plants containing harmful chemicals can be identified to the user and their use can be prevented. Transferring the algorithm from the computer system to mobile applications by covering more plant varieties will be a guide for future studies.

Özet

Amaç: Bu çalışmanın amacı herhangi bir bitkiyi morfolojik özellikleri; yani yaprak biçimi, rengi ya da kokusu gibi özellikleriyle tanımlayarak, görüntü işleme ve makine öğrenmesi yöntemiyle sınıflandırmaktır. Yöntem: Bu çalışmada kaggle adlı açık erişimli veri tabanından elde edilen bitki görüntüleri makine öğrenimi için kaynak olarak kullanıldı. Görüntü öğrenme işlemi yapıldıktan sonra bitkilerin yaprak görüntüleri Evrişimli Sinir Ağı (CNN) yöntemi ile sınıflandırıldı. Sisteminin çalıştığını doğrulamak için iki farklı bitkinin her biri için 100 adet yaprak ve çiçek görüntüsü alınarak Görüntü Veri Üreteci algoritması ile eldeki istatiksel verinin sayısı 700’e arttırıldı. Bulgular: Sisteminin bitkileri % 97 doğrulukla tanımladığı sonucuna varılmıştır. Makine öğrenimi algoritmasının performansı karışıklık matrisinden de anlaşılabilir. Bu çalışmada izlenen yöntemde karışıklık matrisinin köşegenel elemanları 98 ve 79 elde edilmiştir. Bu da uyguladığımız metodun istatistiksel olarak anlamlı olduğunu belirtmektedir. Sonuç: Bu çalışmada kullanılan algoritma sayesinde geleneksel ve tamamlayıcı tıpta kullanılan bitkilerin kimliklemesi %97 doğrulukla yapılabilmiştir. Bu algoritma ile içeriğinde zararlı kimyasalların olduğu bitkiler kullanıcısına tanımlanabilir ve kullanmaları engellenebilir. Algoritmanın daha fazla bitki çeşidini kapsayarak bilgisayar sisteminden mobil uygulamalara aktarılması ileriki çalışmalar için yol gösterici olacaktır.

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Cite This Article

SÖĞÜT, F., REŞİTOĞLU, B., KANGAL, E. E. (2023). A Technological Perspective on Traditional Medicine: Classification of Plant Species with Machine Learning. Mersin Üniversitesi Tıp Fakültesi Lokman Hekim Tıp Tarihi ve Folklorik Tıp Dergisi, Cilt: 13 Sayı: 3, 764–774. https://ajindex.com/articles/963059

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