Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

Wei, Kaihua; Chen, Bojian; Li, Zejian; Chen, Dongmei; Li, Guangyu; Lin, Hongze; Zhang, Baihua

doi:10.3390/s22207764

Cited by 15 publications

(7 citation statements)

References 44 publications

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“…The findings of the research support this statement. For example the classification performance of fluorescence images was reported to be better than that of reflectance images, as shown by Wei [41] where fluorescence images had a higher accuracy of 97.5% in the process of classifying tea leaves. This research contribution is to determine the best deeplearning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification.…”

Section: Introductionmentioning

confidence: 99%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”. 300 images were acquired through a mini studio equipped with UV lamps. The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.

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Section: Introductionmentioning

confidence: 99%

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Sadimantara,

Argo,

Sucipto

et al. 2024

Journal of Robotics and Control

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“…Their method involving background removal and CLAHE preprocessing achieved 100% accuracy in flower classification and 97% in stem and leaf classification. Wei et al. (2022) trained on both fluorescence and white light images of five tea varieties using VGG16 and ResNet34.…”

Section: Introductionmentioning

confidence: 99%

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model

Pan,

Yu,

Dong

et al. 2024

Front. Plant Sci.

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IntroductionIn agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues.MethodsG-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11’s fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency.ResultsRigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image.DiscussionThis validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.

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“…The adoption of LED lamps as an alternative excitation source for inducing fluorescence signals has been gaining popularity. With LEDs' capability to generate varied fluorescence signals, the acquired images provide richer information from each image sample [12,13]. The utilization of this technology is anticipated to enhance accuracy and precision in ANN modeling within the context of piperine content measurement.…”

Section: Introductionmentioning

confidence: 99%

Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

Sandra,

Damayanti,

Juniar Nainggolan

et al. 2024

BIO Web Conf.

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The conventional method for determining piperine content involves a series of labor-intensive steps, including drying the pepper samples, grinding them, and then extracting them using high-grade ethanol through a reflux method. While effective, this process is time-consuming and resource-intensive, posing limitations in terms of efficiency and the ability to address potential variations. Therefore, there is an urgent need to explore more efficient and rapid approaches for accurately measuring and predicting piperine content, with machine learning approach. This research aims to explore the potential of using fluorescence imaging methods and ANN models to increase the efficiency of measuring piperine content on Javanese long pepper. We propose a machine learning approach using UV-induced fluorescence imaging of Javanese long pepper. UV LEDs (365 nm) induced fluorescence, with color variation indicating piperine content. An artificial neural network (ANN) model, trained on color texture features from fluorescence images, predicted piperine content, achieving an R2 value of 0.88025 with ten selected features using the One-R attribute. The final ANN, configured with 'trainoss' learning, 'tansig' activation, 0.1 learning rate, and 10-40-10 nodes, demonstrated a testing R2 of 0.8943 and MSE of 0.0875. LED-induced fluorescence enhances machine learning's piperine content prediction. This research contributes to more efficient piperine content measurement methods.

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Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

Cited by 15 publications

References 44 publications

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning

Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model

Predicting piperine content in javanese long pepper using fluorescence imaging and machine learning model

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