Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/13762
  CropHealthNet: Potato Disease Detection Using Depthwise Separable Convolutional Neural Networks

  Catal Reis, Hatice

  Plant diseases are one of the primary factors that threaten food security, crop yields, harvesting, and sustainable production in the agricultural sector. In this context, early disease diagnosis is essential for enhancing agricultural productivity and reducing crop losses. In modern agricultural practices, machine learning-based approaches offer effective and innovative solutions for detecting and classifying plant diseases. These modern and innovative methods generally reach high accuracy rates with large-scale image datasets. In particular, deep learning algorithms have achieved significant success in modeling disease’s spread dynamics and detecting early disease symptoms. In this study, a convolutional neural network (CNN)-based, lightly parallel deep learning model called CropHealthNet is proposed for classifying potato leaf diseases. The model is designed with special depthwise separable convolution-based convolution layers to reduce the computational cost of CNN. CropHealthNet is a low-parameter model suitable for real-time operation, capable of recognizing disease-specific patterns with high accuracy. CropHealthNet aims to achieve deep and comprehensive feature extraction through its parallel structure and varying channel numbers. The features from each parallel structure are combined at multiple levels, increasing the model’s ability to detect abstract and deep patterns. Moreover, the proposed model has extremely low computational complexity with only 0.47 million parameters and 0.59 GFLOPs, thus offering high efficiency compared to existing approaches. The effectiveness of the proposed model was evaluated using the PlantVillage Potato Dataset. Since the PlantVillage Potato Dataset has an unbalanced data distribution, the number of data for each class has been equalized using the Synthetic Minority Over-sampling Technique (SMOTE) to evaluate the model’s performance against data imbalance. The proposed model succeeded in achieving 99.30% and 99.50% accuracy rates in the tests conducted with unbalanced and balanced datasets, respectively. Experimental findings show that the proposed model generalizes well despite data imbalance and correctly classify diseases on potato leaves. Therefore, the model can demonstrate strong performance in agricultural practices and can detect plant diseases under real-world conditions.

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  İncele
• MakaleAçık Erişim2024/r/gumushane/14075
  Advanced Tomato Disease Detection Using the Fusion of Multiple Deep-Learning and Meta-Learning Techniques

  Catal Reis, Hatice

  Automatic and early detection of diseases in crops is important for producers in terms of product yield. Methods are needed to diagnose the disease and select interventions for tomato leaf. This study aims to create a novel framework for automatically extracting tomato leaf disease. The recommended method automatically detects diseases in three classes: healthy, early, and late blight. This study developed a multi-deep and meta-learning fusion framework that automatically combines modern deep-learning algorithms and meta-classifiers to detect tomato leaf disease. The study includes CNN, transformer, and MLP-based models previously trained with the ImageNet dataset and voting classifier (VtC) methods, which are meta-classifiers. Hybrid approaches have been developed using the VtC method and deep learning algorithms such as CoAtNet, FasterNet, FastViT, WaveMLP, and DenseNet201. In addition to the proposed hybrid framework, different types of deep learning models based on CNN, transformer, MLP, transfer learning, and the dataset improved by the CLAHE technique and the original dataset were used to diagnose tomato leaf diseases. While CNN-based modern architectures include DenseNet201, MobileNet, LCNet, GhostNet, CoAtNet, ConvMixer, and FasterNet, transformer architectures include BeiT, FlexiViT, MaxViT, FastViT, and MLP-based architectures include MLP mixer and WaveMLP models. In the classification performed with original images, the proposed method produced incorrect results in only five images among 900 test data. In the classification made with CLAHE images, the proposed hybrid framework produced the most successful result in this study, producing incorrect results in only four images.

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