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Shrimp farming is a key sector in economic development in Mekong Delta provinces. Unfortunately, there are many problems in shrimp farming, especially shrimp diseases which cause a considerable loss. Shrimp diseases are expressed through symptoms and manifestations of shrimp. Recognizing the importance of shrimp symptoms to help raise an early warn...
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... order to effectively prevent shrimp diseases, it is common for shrimp farmers to perform daily monitoring and understanding of signs of shrimp diseases in order to effectively detect and prevent them. In particular, the detection can be investigated through the states on shrimp (Figure 1) or through disease symptoms. For example shrimp eats a lot of abnormality for a few days, then stops eating. ...
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Citations
... Because of the above reason, we propose a novel approach to detect shrimp diseases from text, based on the textual symptom descriptions that can be obtained from various sources. There are also a few papers that have performed textbased classification of shrimp diseases, but they only used a few machine learning models and achieved not high results [32]. Therefore, we conduct this research with different machine learning models and deep learning models (SVM, Logistic Regression, Multinomial Naive Bayes, Bernoulli Naive Bayes, Random forest, DNN, LSTM, GRU, BRNN and RCNN), and improve the accuracy. ...
... In studies on crops [29], [30], rice [31] have achieved promised accuracy (over 90%) by disease description and support chatbot system. In addition, research [32] has shown the first steps in approaching using NLP to identify diseases in shrimp with basic machine learning techniques with an accuracy of over 80%. It proved that the use of NLP in diagnosing of shrimp diseases is essential. ...
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.
... Their research results showed that the SVM perform well with 94% accuracy. In addition to fish classification, authors in [9] implemented a shrimp disease classification using SVM with accuracy of 81.27%. ...
... They converted the video data Liu et al showed another system [10] used deep convolutional neural networks (Deep-ShrimpNet) and to normalise the images, they used multiple image processing techniques to differentiate between soft-shell shrimp and sound shrimp. Quach et al showed another system, [14]. In this paper, the authors take a different approach. ...
Background: Fish diseases are the primary cause of decreased productivity and financial losses in fish farms. Detecting and monitoring fish disease using human eye is a time-consuming manual process. Technological advancements have made possible that data may now be collected at unprecedented speeds, quantities, and complexity, with far less effort and expense. Species must be provided optimum environmental conditions for healthy production. However, sub-optimal conditions and managerial issues could lead to disease outbreaks in fish farms. Machine learning (ML) classifiers can provide solutions to fish farm difficulties by collecting data with less efforts. Methods: This study investigated water physico-chemical parameters potentially responsible for bacterial disease outbreak in fish farms. Four most popular ML algorithms, i.e., support vector machine (SVM), naïve bayes (NB), random ferns (RFerns), and K-nearest neighbor (kNN) were used to detect the physico-chemical parameters of water causing the disease. Data were collected from 3 different farms in two-month periods for 1 year. Models were developed by using 10-fold cross validation procedure to the training dataset data for each model. The models were examined using seven distinct metrics throughout the training and testing phases. Results: The SVM and RFerns classifiers produced accurate results (100% for both) during the testing phase, while kNN and NB classifiers achieved lower accuracy (91.3% accuracy for both). Conclusion: The SVM and RFerns algorithms performed better than kNN and NB algorithms in both the training and testing phases of the study. Although earlier research confirms the efficacy of the SVM algorithm in aquaculture, comparable efficacy of RFerns with SVM has been reported in this sector for the first time, which is a significant addition to the literature.
Soft crab is an export product where foreign demand is much higher than production. In the production of soft crabs, it is done by keeping the crabs individually in a crab box which is placed in the pond until they molt. Molting is a natural process of molting, i.e. removing the old tough skin for growth purposes. Shortly after molting, the new crab shells are still very soft and will harden again after water absorption occurs. Therefore it is important to monitor molting crabs to help farmers in the cultivation of soft shell crabs. The number of crab datasets is 1060 which consists of 1000 training data and 60 testing data. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest Classifier (RFC). KNN, SVM, and RFC are classification algorithms from Machine Learning. This study aims to compare the performance of the three algorithms so that the performance of the three algorithms is known. Several parameters are used to configure the KNN, SVM, and RFC algorithms. From the results of the trials conducted, KNN has the best performance with 98.33% accuracy, 98.33% precision, 98.38% recall, and 98.38% F1 Score.
