Multi-Input Convolutional Neural Network for Flower Grading

Sun, Yu; Zhu, Lin; Wang, Guan; Zhao, Fang

doi:10.1155/2017/9240407

Cited by 52 publications

(29 citation statements)

References 14 publications

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“…GAP greatly reduces the number of trainable parameters and make the model lighter and thus alleviate overfitting. Multi-input models [25][26][27][28] is another kind of nonsequential network topology, and it has multiple input layers which can make full use of multimodal or multiple types of data. Nickfarjam and Ebrahimpour-Komleh [25] adopt the deep belief networks with multi-input topology to conduct shape-based human action classification and improved model performance.…”

Section: Related Workmentioning

confidence: 99%

Deep Ensemble Learning for Human Action Recognition in Still Images

Zhang

et al. 2020

Complexity

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Numerous human actions such as “Phoning,” “PlayingGuitar,” and “RidingHorse” can be inferred by static cue-based approaches even if their motions in video are available considering one single still image may already sufficiently explain a particular action. In this research, we investigate human action recognition in still images and utilize deep ensemble learning to automatically decompose the body pose and perceive its background information. Firstly, we construct an end-to-end NCNN-based model by attaching the nonsequential convolutional neural network (NCNN) module to the top of the pretrained model. The nonsequential network topology of NCNN can separately learn the spatial- and channel-wise features with parallel branches, which helps improve the model performance. Subsequently, in order to further exploit the advantage of the nonsequential topology, we propose an end-to-end deep ensemble learning based on the weight optimization (DELWO) model. It contributes to fusing the deep information derived from multiple models automatically from the data. Finally, we design the deep ensemble learning based on voting strategy (DELVS) model to pool together multiple deep models with weighted coefficients to obtain a better prediction. More importantly, the model complexity can be reduced by lessening the number of trainable parameters, thereby effectively mitigating overfitting issues of the model in small datasets to some extent. We conduct experiments in Li’s action dataset, uncropped and 1.5x cropped Willow action datasets, and the results have validated the effectiveness and robustness of our proposed models in terms of mitigating overfitting issues in small datasets. Finally, we open source our code for the model in GitHub (https://github.com/yxchspring/deep_ensemble_learning) in order to share our model with the community.

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Section: Related Workmentioning

confidence: 99%

Deep Ensemble Learning for Human Action Recognition in Still Images

Zhang

et al. 2020

Complexity

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“…We aim at developing a versatile visual inspection algorithm that is easy to configure and fast to perform grading tasks for our embedded smart camera. As discussed in the introduction, more sophisticated but powerful machine learning [12][13][14][15][16] and deep learning [17][18][19][20] approaches have been successfully applied to fruit and vegetable grading. Most of them are not suitable for embedded applications because of their computational complexity.…”

Section: Visual Inspection Algorithmmentioning

confidence: 99%

“…Artificial neural networks have been successfully used for sorting pomegranate fruits [13], apples [14], fruit grading based on external appearance and internal flavor [15], and color-based fruit classification [16]. Some researchers explored and applied deeper neural networks, such as CNN, to fruit and vegetable grading [17][18][19] and flower grading [20] and achieved great success. With different degrees of complexity, all these methods significantly advanced the development of machine vision technology.…”

mentioning

confidence: 99%

Smart Camera for Quality Inspection and Grading of Food Products

et al. 2020

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Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper.Electronics 2020, 9, 505 2 of 18 or sorting machines for food products on the market have been designed to automate these tasks and have proven to be very successful.Many recent developments were aimed at broad grading applications. For example, a machine learning-based system was built to use color and texture features for tomato grading and surface defect detection [1]. Color and texture features were collected for maturity evaluation, defect detection, size and shape analysis for mangoes [2]. Guava fruits were classified into four maturity levels using the K-nearest neighbor (KNN) algorithm to analyze color distribution in HSI (Hue, Saturation, and Intensity) color space [3]. Image processing techniques were used for corn seed grading [4], plum fruit maturity evaluation [5], quality assessment of pomegranate fruits [6], and the ripeness of palm fruit [7]. Two grading systems for date maturity [8] and skin quality [9] evaluation were designed specifically for Medjool dates. All these recent developments were designed for their unique grading applications. Although some may be easier than others to adapt for similar applications, minor adjustments are needed even for a simple generic solution such as color grading or shape analysis.A comprehensive review of fruit and vegetable quality evaluation systems revealed that many systems use slightly different handcrafted features for even the same applications [10]. For example, twenty-six different color features in four different color spaces and twenty-two texture fe...

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“…A grid form model was evaluated to assess objectives and a genetic algorithm was used produce an optimal solution [53] . Flower grading was also done by using a similar concept [54] .…”

Section: Cnn Applications In Smart Agriculturementioning

confidence: 99%

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

Zhu¹,

Liu²,

Liu³

et al. 2018

International Journal of Agricultural and Biological Engineering

141

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In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

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Multi-Input Convolutional Neural Network for Flower Grading

Cited by 52 publications

References 14 publications

Deep Ensemble Learning for Human Action Recognition in Still Images

Deep Ensemble Learning for Human Action Recognition in Still Images

Smart Camera for Quality Inspection and Grading of Food Products

Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

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