Efficient deep features selections and classification for flower species recognition

Çıbuk, Musa; Budak, Ümit; Guo, Yanhui; İnce, Melih Cevdet; Şengür, Abdulkadir

doi:10.1016/j.measurement.2019.01.041

Cited by 82 publications

(25 citation statements)

References 19 publications

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“…The average flower recognition accuracy rate was 97.78% for the proposed CNN model, which is higher than those of other advanced classifiers. For flower species classification, Cıbuk et al 61 employed the concatenated AlexNet and VGG-16 models to extract features, which were then used as the input to the minimum redundancy maximum relevance (mRMR) method for selecting some higher abstract features. The selected abstract features were fed into the SVM classifier that was combined with a radial base function (RBF) kernel to obtain the final classification results.…”

Section: Applications Of Deep Learning In Horticulture Cropsmentioning

confidence: 99%

Applications of deep-learning approaches in horticultural research: a review

Yang

2021

Hortic Res

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Deep learning is known as a promising multifunctional tool for processing images and other big data. By assimilating large amounts of heterogeneous data, deep-learning technology provides reliable prediction results for complex and uncertain phenomena. Recently, it has been increasingly used by horticultural researchers to make sense of the large datasets produced during planting and postharvest processes. In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks. We described in detail the application scenarios reported in the relevant literature, along with the applied models and frameworks, the used data, and the overall performance results. Finally, we discussed the current challenges and future trends of deep learning in horticultural research. The aim of this review is to assist researchers and provide guidance for them to fully understand the strengths and possible weaknesses when applying deep learning in horticultural sectors. We also hope that this review will encourage researchers to explore some significant examples of deep learning in horticultural science and will promote the advancement of intelligent horticulture.

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Section: Applications Of Deep Learning In Horticulture Cropsmentioning

confidence: 99%

Applications of deep-learning approaches in horticultural research: a review

Yang

2021

Hortic Res

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“…As Back Propagation (BP) ANN has a strong nonlinear mapping ability, flexible network structure, simple theoretical basis, and wide application range, it was used to first partition the training data. After data classification, the Radial Basis Function (RBF) ANN was used to fit the relationship between the magnetic induction intensity and the rotation angle in the space of the ball joint [7][8][9]. RBF ANN was chosen because it can approximate any nonlinear function with arbitrary accuracy and has the unique best approximation characteristic [10][11][12].…”

Section: Selection Of Annmentioning

confidence: 99%

A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm

Zhang

et al. 2019

Applied Sciences

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The rotation orientation and the angle of precision of an intelligent ball joint cannot be automatically obtained in passive motion. In this paper, a new method based on a Hall sensor with a permanent magnet (PM) is proposed to identify the spatial rotation orientation and angle. The basic idea is to embed a PM on a ball while the Hall sensors are arrayed into the ball socket. When the ball rotates, the Hall sensor array detects the variation of the magnetic induction intensity in space. By establishing a mathematical model between the variation of the magnetic induction intensity and the orientation and angle of rotation, the rotation angle in the space where the ball is located can be inversely solved. The establishment of the theoretical model is based on the theory of the equivalent magnetic charge method, which has a few native defects that cannot be overcome by itself. This paper presents the relationship between the magnetic induction intensity change and the rotation angle of the ball in space, which was constructed by an artificial neural network (ANN) and will simplify the mathematical model, shorten the operation time, and improve the efficiency of real-time detection. Based on the simulation analysis, the optimal matching scheme between the PM and the magnetic effect sensor was determined, and the structural parameters of the ball joint prototype were optimized. The data training and comparison test of the neural network model were completed on a self-developed calibration device. The experimental results show that for a ±20 • measurement range, the average errors of the uniaxial measurements are 1 51 and 1 55 on the two axes, respectively. At present, the measurement accuracy of the prototype is still relatively low; however, this idea of modeling based on ANN removes the shackles of mathematical modeling, reminding us that we can consider the design of sensors or complete geometric measurement modeling from a new perspective.

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“…According to the results obtained from deep learning-based studies, approximately a 99% performance level was established for the Flavia, Foliage, Swedish, and Folio datasets. On the other hand, for the Flower17 and Flower102 datasets, ( Cıbuk et al., 2019 ) achieved highest accuracy scores of 96.39% and 95.70%, respectively. As a result, studies based on deep learning have provided superior performance over traditional methods.…”

Section: Introductionmentioning

confidence: 99%

A multi-division convolutional neural network-based plant identification system

Türkoğlu

Aslan

Arı

et al. 2021

PeerJ Computer Science

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Background Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flower17, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.

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Efficient deep features selections and classification for flower species recognition

Cited by 82 publications

References 19 publications

Applications of deep-learning approaches in horticultural research: a review

Applications of deep-learning approaches in horticultural research: a review

A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm

A multi-division convolutional neural network-based plant identification system

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