Akademik Veri Yönetim Sistemi
International Research Journal of Engineering and Technology (IRJET), cilt.11, sa.5, ss.853-861, 2024 (Hakemli Dergi)
Understanding
plant biology and classification of plant species stands out as an important
issue in the field of biology. In recent years, with advancements in the field
of artificial intelligence, the use of artificial intelligence for plant
classification has increased. In this context, plant leaf images have begun to
be examined with artificial intelligence. However, in the classification of
plant species using artificial intelligence, the use of cell images may provide
more accurate and reliable results compared to leaf images. Cell images allow
for a closer focus on the genetic structure and fundamental characteristics of
the plant, whereas leaf images may be more sensitive to environmental
variability. Therefore, in plant classification using artificial intelligence,
analyses based on cell images are preferred. In this study, microscopic cell
images of four different plant species (Ficus Benjamin, Spathiphyllum, Ficus
Elastica and Anthurium) were classified using machine learning methods such as
KNN, SVM, Logistic Regression, Decision Trees and Random Forest. In order to
classify plant species, a new data set consisting of microscopic cell images of
four different plant species was created. Using this data set, plant species
were classified with five different machine learning methods and their success
accuracies were compared. As a result of the comparison, the best plant species
classification was obtained by Random Forest with a success rate of 96.74%, and
the worst plant species classification was obtained by the KNN method with a
success rate of 86.05%. According to the results obtained, it was seen that
microscopic plant cell images were successfully classified using machine
learning methods.