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Fisher's linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle (θ), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD models. Z and θ of a parallel-plate capacitance system, h...
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The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms....
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... In Table 8 below, the prominent studies in the literature and this study are presented comparatively. When the studies in the table are examined, species identification was performed according to the signal frequency obtained from impedance measurement hardware [22]. Species identification was performed by extracting deep features of wheat images obtained with hyper-spectroscopy imaging over Near Infrared (NIR) images [12]. ...
Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated.
... www.nature.com/scientificreports/ agricultural fields, including research into wheat 17 , soybean 18 , cowpea 19 and rice 12 production. So far, there are few reports on the application of NIR spectroscopy in forestry and pine nut research. ...
Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts ( Pinus bungeana , Pinus yunnanensis , Pinus thunbergii , Pinus armandii , Pinus massoniana , Pinus elliottii and Pinus taiwanensis ) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.
... Morphological analysis requires that the appraiser has extensive experience, and the accuracy is low for two or more varieties with little morphological difference [10]. Although the chemical analysis method has a high identification accuracy, it is destructive to the sample, time consuming, and so difficult to operate that nonprofessionals are not competent to perform it [11]. ...
Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.
... Sensors 2019, 19, 5225 2 of 15 reagents, which causes chemical contamination; thus, these methods are not suitable for large-scale sample detection [10]. ...
Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.
