Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Yasmin, Jannat; Lohumi, Santosh; Ahmed, Mohammed Raju; Kandpal, Lalit Mohan; Faqeerzada, Mohammad Akbar; Kim, Moon Sung; Cho, Byoung‐Kwan

doi:10.3390/s20092690

Cited by 16 publications

(6 citation statements)

References 45 publications

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“…The obtained spectra of nuts presented in Figure 2 a primarily correspond to a combination of O−H stretching, C−H bending, and C−O stretching. Bands in the range of 3100–3000 cm –1 represent the stretching vibration of the C=CH cis-olefinic groups of unsaturated fatty acids present in almond [ 24 , 25 , 26 ].…”

Section: Resultsmentioning

confidence: 99%

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Faqeerzada

Lohumi

Joshi

et al. 2020

Foods

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Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.

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Section: Resultsmentioning

confidence: 99%

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Faqeerzada

Lohumi

Joshi

et al. 2020

Foods

Self Cite

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“…This method achieved a detection accuracy of 82% for five distinct types of sugar beet damage. In [ 20 ], the authors proposed a machine vision-based, one-class classification method for evaluating the quality of tomato seeds. A 97% accuracy rate was achieved in classifying healthy and infected seeds.…”

Section: Related Workmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

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The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

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“…Image analysis and spectral techniques are two non-destructive techniques for evaluating seed quality that are commonly used in agriculture. The machine vision system can provide robust, reliable, and rapid results [10]. Machine vision was used in tomato seed classification, among others, to discriminate healthy seeds from infected ones and foreign materials with a correctness of 97.7% [10].…”

Section: Introductionmentioning

confidence: 99%

“…The machine vision system can provide robust, reliable, and rapid results [10]. Machine vision was used in tomato seed classification, among others, to discriminate healthy seeds from infected ones and foreign materials with a correctness of 97.7% [10]. Ropelewska and Piecko [11] discriminated tomato seeds belonging to five cultivars with an accuracy of 83.6%, and two tomato seeds cultivars were classified with a correctness reaching 99.75%.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Sabancı

2023

Sustainability

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In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.

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Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Cited by 16 publications

References 45 publications

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

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