Transfer Öğrenme Temelli Bitki Yaprak Hastalıklarının Tespiti İçin Karşılaştırmalı Bir Çalışma

Sevde Sazak; Selin Ceren Balsak; Hasan Badem

doi:10.18016/ksutarimdoga.vi.1571202

TR EN

Transfer Öğrenme Temelli Bitki Yaprak Hastalıklarının Tespiti İçin Karşılaştırmalı Bir Çalışma

Öz

Bitkilerin sağlıklı bir şekilde yetiştirilmesi ve verimli ürün alınması için hastalıkların erken teşhisi kritik öneme sahiptir. Bitki hastalıklarının bir çiftçi tarafından görsel olarak tanımlanması genellikle zordur. Ancak, makine öğrenmesi yöntemleri kullanılarak, bitki hastalıkları tespiti sürecini daha hızlı ve hassas bir şekilde gerçekleştirilebilir. Bu sayede, ürün kayıplarını azaltarak, maliyetlerinin düşürülmesi ve tarımsal üretkenliğin artırılmasıyla genel ekonomik verimliliği yükseltebilmek mümkündür. Bu çalışmada, 12 farklı sağlıklı bitki ve 30 farklı hastalıkla bulaşık bitki yaprağı görüntüleri kullanılarak bitki hastalıklarının yapay zeka ile sınıflandırması amaçlanmıştır. Geliştirilen sistemde yapay zeka modeli olarak VGG16, VGG19, AlexNet, MobileNetV1 ve MobileNetV2 olmak üzere 5 farklı Evrişimli sinir ağı modeli kullanılmıştır. Tüm modeller eğitilmiş ve doğruluk değerleri üzerinden karşılaştırılmıştır. MobileNetV1 üzerinden %99,20 ile en yüksek doğruluk değeri elde edilmiştir. Önerilen yöntem, çeşitli performans analizlerinden geçirilerek doğrulanmıştır. Yapay zeka tabanlı bir web uygulama da son kullanıcı için geliştirilmiştir.

Anahtar Kelimeler

A Comparative Study for Detection of Plant Leaf Diseases Based on Transfer Learning

Abstract

Early diagnosis of diseases is critical for growing plants in a healthy manner and obtaining productive products. Plant diseases are generally difficult to visually identify by a farmer. However, by using machine learning methods, the process of detecting plant diseases can be realized more quickly and precisely. Hence, it can reduce product losses, reduce costs, and increase overall economic efficiency by increasing agricultural productivity. In this study, classifying plant diseases with artificial intelligence has been aimed by using images obtained from 12 different images of healthy plants and plant leaves infected with 30 different diseases. In the developed system, 5 different Convolutional neural networks (CNN) models including VGG16, VGG19, AlexNet, MobileNetV1, and MobileNetV2, have been used as artificial intelligence models. All models have been trained and compared based on their accuracies. The highest accuracy value of 99.20% has been obtained by The MobileNetV1. The proposed method has been validated through various performance analyses. An artificial intelligence-based web-based application has also been developed for the end-user.

Keywords

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Details

Primary Language

Turkish

Subjects

Plant Protection (Other)

Journal Section

Research Article

Authors

Sevde Sazak
0009-0000-9035-3261
Türkiye

Selin Ceren Balsak
0000-0002-2326-7520
Türkiye

Hasan Badem ^*
0000-0002-4262-8774
Türkiye

Early Pub Date

January 30, 2025

Publication Date

February 12, 2025

Submission Date

October 21, 2024

Acceptance Date

December 4, 2024

Published in Issue

Year 2025 Volume: 28 Number: 1

DOI

https://doi.org/10.18016/ksutarimdoga.vi.1571202

IZ

https://izlik.org/JA88WC75HP

Cite

RIS / Bibtex

APA

Sazak, S., Balsak, S. C., & Badem, H. (2025). Transfer Öğrenme Temelli Bitki Yaprak Hastalıklarının Tespiti İçin Karşılaştırmalı Bir Çalışma. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 28(1), 154-170. https://doi.org/10.18016/ksutarimdoga.vi.1571202