Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.
We thank Amasya University and Bandırma Onyedi Eylül University for providing the opportunity to use computer laboratories in the realization of this study.
Primary Language | English |
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Subjects | Computer Software |
Journal Section | Articles |
Authors | |
Early Pub Date | April 27, 2024 |
Publication Date | April 30, 2024 |
Submission Date | September 12, 2023 |
Acceptance Date | January 10, 2024 |
Published in Issue | Year 2024Volume: 7 Issue: 1 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License