Design of a P. Vannamei White Spot Syndrome Virus (WSSV) Detection System Utilizing YOLOv5n

Amora, Dennis Jefferson A.; Alulod, Daniel Psalm M.; Debolgado, Kimberly Ann B.; Magcale, John Ronald M.; Tobias, Christine Ruth A.; Arenas, Shearyl U.

doi:10.1109/iet-iceta56553.2022.9971656

Cited by 1 publication

(1 citation statement)

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“…This method can be divided into different categories. The first is an image-based shrimp disease classification method using deep learning models such as Convolutional Neural Network (CNN) [12], [13], [21], YOLO model [22], and machine learning [23], [24]. The effectiveness of this strategy depends mainly on the picture size and quality.…”

Section: B Methods Of Using Artificial Intelligencementioning

confidence: 99%

Using the Term Frequency-Inverse Document Frequency for the Problem of Identifying Shrimp Diseases with State Description Text

Quach¹,

Quynh²,

Quoc³

et al. 2023

IJACSA

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With the increasing demand for research on shrimp disease recognition to assist far-off farmers who need the proper assistance for their shrimp farming, shrimp disease prediction research is still in the initial stage. Most current methods utilize vision-based models, which mainly face challenges: symptom detection and image quality. Meanwhile, there are few researches which are language-based to get over the issues. In this study, we will experiment with natural language processing based on recognizing shrimp diseases; based on descriptions of shrimp status. This study provides an efficient solution for classifying multiple diseases in shrimp. We will compare different machine learning models and deep learning models (SVM, Logistic Regression, Multinomial Naive Bayes, (a4) Bernoulli Naive Bayes, Random forest, DNN, LSTM, GRU, BRNN, RCNN) in terms of accuracy and performance. The study also evaluates the TF-IDF technique in feature extraction. Data were collected for 12 types of shrimp diseases with 1,037 descriptions. Firstly, the data is preprocessed with standardised Vietnamese accent typing, tokenized words, converted to lowercase, removed unnecessary characters and stopwords. Then, TF-IDF is utilized to express the text feature weight. Machine learning-based and deep learning-based models are trained. The experimental results show that Random forest (F1-Score micro: 98%) and DNN (Validation accuracy: 84%) are the most efficient models.

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