PLA nanocomposites with antimicrobial action, based on olive fruit polyphenols and citrus fruit extracts encapsulated in Maltodextrin

Tudose, Ioan Valentin; Roșca, Irina; Romanițan, Cosmin; Ionescu, Octavian; Petrotos, Konstantinos; Zaoutsos, Stefanos; Suchea, M.; Koudoumas, E.

doi:10.1109/cas52836.2021.9604116

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…There were 50 epochs in which the model performed ideally, as shown in Figure 5. Due to the improved performance, [Tudose, 2021] implemented a CNN dropout (2.6%). As a consequence, the experiment employed dropout rates of 0.24 and 0.52%, with the 0.5% dropout rate producing the best results out of the three potential possibilities.…”

Section: Figure 4: Effect Of Color and Augmentation On The Accuracy O...mentioning

confidence: 99%

Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture

Saini¹,

Bhatnagar²,

Srivastava³

2022

jucs

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Machine learning is a branch of computer science concerned with developing algorithms & models capable of ‘learning through data and iterations’. Deep learning simulates the structure and function of human organs and diseases using artificial neural networks with more than one hidden layer. The primary purpose of this work is to develop and test computer vision and machine learning algorithms for classifying Huanglongbing (HLB)-infected, healthy, and unhealthy leaves and fruits of the citrus plant. The images were segmented using a normalized graph cut, and texture information was extracted using a co-occurrence matrix. The collected attributes were used for classification and support vector machine (SVM), and deep learning methods were employed. When rating the classification outcomes, the accuracy of the classification and the number of false positives and false negatives were considered. The result shows that Deep Learning could create categories up to 96.8% of HLB-infected leaves and fruits. Despite a broad variance in intensity from leaves collected in North India, this method suggests it could be beneficial in diagnosing HLB.

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Section: Figure 4: Effect Of Color and Augmentation On The Accuracy O...mentioning

confidence: 99%

Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture

Saini¹,

Bhatnagar²,

Srivastava³

2022

jucs

View full text Add to dashboard Cite

show abstract

An Automatic Detection of Citrus Fruits and Leaves Diseases Using CNN

Suji,

et al. 2024

2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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PLA nanocomposites with antimicrobial action, based on olive fruit polyphenols and citrus fruit extracts encapsulated in Maltodextrin

Cited by 2 publications

References 11 publications

Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture

Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture

An Automatic Detection of Citrus Fruits and Leaves Diseases Using CNN

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PLA nanocomposites with antimicrobial action, based on olive fruit polyphenols and citrus fruit extracts encapsulated in Maltodextrin

Cited by 2 publications

References 11 publications

Automatic Detection and Recognition of Citrus Fruit &amp; Leaves Diseases for Precision Agriculture

Automatic Detection and Recognition of Citrus Fruit &amp; Leaves Diseases for Precision Agriculture

An Automatic Detection of Citrus Fruits and Leaves Diseases Using CNN

Contact Info

Product

Resources

About

Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture

Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture