Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024/r/gumushane/14384
  Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection

  Catal Reis, Hatice; Turk, Veysel

  Early detection of plant diseases is critical to prevent disease spread and assist farmers. Thanks to their high discrimination ability, Convolutional Neural Network (CNN)-based architectures can offer practical solutions in identifying different plant diseases. This study proposes a combined method to classify wheat plant diseases using the Integrated Deep Learning Framework (IDLF) and ensemble learning (EL) model. The proposed method uses pre-trained deep neural networks (ImageNet -based model (w/pre-training)). In addition, in the classification process, the performance of hybrid methods consisting of 13 deep learning architectures (DLA) trained from scratch, pre-trained DLA, deep, and-machine learning models were analyzed. Moreover, the use of hypercolumn, contrast stretching, and “Contrast Limited Adaptive Histogram Equalization (CLAHE)” techniques are used to improve the image quality of the dataset. It is aimed to obtain a strong classification performance by combining these methods. In the experimental process, in the study using the original dataset, the RegNetY080 model trained from scratch had an accuracy value of 97.64%; in the study with the optimized dataset with CLAHE, an accuracy value of 98.33%; in the study performed with the CLAHE-improved dataset and transfer learning method, an accuracy value of 99.58% was obtained. An accuracy of 99.58% was achieved in the proposed hybrid study with the pre-trained RegNetY080 model and Random Forest (RF). The proposed method using IDLF and EL model provided 99.72% accuracy. When the proposed method was applied to the Wheat Leaf Dataset and Wheat Leaf Disease dataset, 97.56% accuracy was obtained for the Wheat Leaf Dataset and 99.43% for Wheat Leaf Disease. The results of this study can be helpful to experts and farmers in the detection of plant diseases.

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• MakaleYalnız künye2011/r/gumushane/15575
  Preconcentration of Cd(II) and Cu(II) ions by coprecipitation without any carrier element in some food and water samples

  Duran, Celal; Ozdes, Duygu; Sahin, Deniz; Bulut, Volkan Numan; Gundogdu, Ali; Soylak, Mustafa

  A simple, rapid, sensitive and environmentally friendly separation and preconcentration procedure, based on the carrier element free coprecipitation (CEFC) of Cu(II) and Cd(II) ions by using an organic coprecipitant, 2-{[4-(4-fluorophenyl)-5-sulphanyl-4H-1,2,4-triazol-3-yl]methyl}-4-{[(4-fluorophenyl) methylene]amino}-5-(4-methylphenyl)-2,4-dihydro-3H-1,2,4-triazol-3-one (MEFMAT) was developed. The analyte ions were determined by flame atomic absorption spectrometric (FAAS) determinations. The optimum conditions for the coprecipitation process were investigated on several commonly tested experimental parameters such as pH of the solution, amount of MEFMAT, sample volume, standing time, centrifugation rate and time. The influences of some anions, cations and transition metals on the recoveries of analyte ions were also investigated, and no considerable interference was observed. The preconcentration factor was found to be 50. The detection limits for Cu(II) and Cd(II) ions based on the three times the standard deviation of the blanks (N:10) were found to be 1.49 and 0.45μgL-1, respectively. The relative standard deviations were found to be lower than 3.5% for both analyte ions. The method was validated by analyzing two certified reference materials (CRM-TMDW-500 Drinking Water and CRM-SA-C Sandy Soil C) and spike tests. The procedure was successfully applied to sea water and stream water as liquid samples and tobacco, hazelnut and black tea as solid samples.

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