Automated color detection in orchids using color labels and deep learning

Abstract

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.

Use of remote sensing and image processing for identification of wild orchids

“…Other researchers have applied machine learning (Sabri et al, 2019;Andono et al, 2021) or neural network algorithms (Arwatchananukul et al, 2020;Apriyanti et al, 2021;Sarachai et al, 2022;Apriyanti et al, 2023;Ou et al, 2023) to extract flower features (e.g., colour, shape) from the library images of orchids. These approaches were also able to obtain high accuracies (82%-99%).…”

An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network

“…Orchids have more than 25,000 species that have spread throughout the world, especially in tropical areas [4], [5]. This plant has beautiful flowers and very diverse shapes, colors and aromas.…”

“…The digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm [13]. Recently machine learning has become widespread research in various aspects, such as spam detection, video recommendation, multimedia concept retrieval, and image classification [5], [14]. Among these algorithms, Deep Learning is often implemented in research [5].…”

“…Recently machine learning has become widespread research in various aspects, such as spam detection, video recommendation, multimedia concept retrieval, and image classification [5], [14]. Among these algorithms, Deep Learning is often implemented in research [5]. Because the deep learning process uses Artificial Neural Networks or artificial reasoning networks which include many layers to explore hierarchical data representation and also complete tasks such as segmentation, regression and classification.…”

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

“…Considering the limitations of processing time, quantity of parameters, classification speed, and hardware computing power of the production line, MobileNet series [18][19][20], ShuffleNet series [16,21], SqueezeNet [22], and other lightweight network architectures have attracted increasing attention in the field of object recognition and classification. In this application, transfer learning [23][24][25] and fine-tuning [26][27] were performed on a lightweight network to obtain a more satisfactory recognition accuracy with fewer parameters. However, the above experiments were not tested in an actual production line, and no specific quantification and comparison of the detection time were illustrated, which cannot accurately reflect the processing speed advantage of the lightweight network architecture in practical applications.…”