Deep convolutional neural network for automatic discrimination between Fragaria × Ananassa flowers and other similar white wild flowers in fields

Abstract

BackgroundThe images of different flower species had small inter-class variations across different classes as well as large intra-class variations within a class. Flower classification techniques are mainly based on the features of color, shape and texture, however, the procedure always involves too many heuristics as well as manual labor to tweak parameters, which often leads to datasets with poor qualitative and quantitative measures. The current study proposed a deep architecture of convolutional neural network (CNN) for the purposes of improving the accuracy of identifying the white flowers of Fragaria × ananassa from other three wild flower species of Androsace umbellata (Lour.) Merr., Bidens pilosa L. and Trifolium repens L. in fields.ResultsThe explored CNN architecture consisted of eightfolds of learnable weights including 5 convolutional layers and 3 fully connected layers, which received a true color 227 × 227 pixels flower image as its input. The developed CNN detector was able to classify the instances of flowers at overall average accuracies of 99.2 and 95.0% in the training and test procedure, respectively. The state-of-the-art CNN model was compared with the classical models of the scale-invariant feature transform (SIFT) features and the pyramid histogram of orientated gradient (PHOG) features combined with the multi-class support vector machine (SVM) algorithm. The proposed model turned out to be much more accurate than the traditional models of SIFT + SVM at overall average accuracies of 82.9 and 55.6% in the training and test procedure and PHOG + SVM at overall average accuracies of 78.3 and 63.1%, respectively.ConclusionsThe proposed state-of-the-art CNN method demonstrates that artificial intelligence is capable of precise classification of the white flower images, whose accuracy is comparable to traditional algorithms. The presented algorithm can be further used for the discrimination of white wild flowers in fields.

The Application of Convolutional Neural Networks in Floristic Recognition

“…The integration of CNNs with Keras enables the development of a robust flower recognition model capable of distinguishing intricate floral characteristics, contributing significantly to the field of computer vision. Lin et al (2018) proposed a deep convolutional neural network for discriminating between Fragaria × Ananassa flowers and other similar white wildflowers in fields. Their CNN architecture consisted of multiple convolutional and fully connected layers, achieving high accuracy in classifying these flowers.…”

“…Their CNN architecture consisted of multiple convolutional and fully connected layers, achieving high accuracy in classifying these flowers. This study highlights the potential of CNNs in differentiating between closely related floral species, emphasizing the importance of deep learning methods in floristic research [8]. Champ, Goëau, and Joly (2016) participated in the LifeCLEF 2016 plant identification challenge, utilizing a Convolutional Neural Network (CNN) approach based on a modified GoogLeNet model.…”

Res-WGAN: Image Classification for Plant Small-scale Datasets

“…This task requires a great deal of expertise; thus, classification becomes more difficult when rare plants are involved. Recently, remarkable progress has been made toward identifying various categories of plant images based on deep learning [2][3][4][5]. Convolutional neural network (CNN) is a typical classification model [6].…”