A5024046088- Water Quality Monitoring Technologies
- Identification and Quantification in Food
- Advanced Chemical Sensor Technologies
- Smart Agriculture and AI
- Water Quality Monitoring and Analysis
King Mongkut's Institute of Technology Ladkrabang
2022-2023
Automatic fish recognition using deep learning and computer or machine vision is a key part of making the industry more productive through automation. An automatic sorting system will help to tackle challenges increasing food demand threat scarcity in future due continuing growth world population impact global warming climate change. As far as authors know, there has been no published work so detect classify moving for culture industry, especially purposes based on species vision. This paper...
Food scarcity is an issue of concern due to the continued growth human population and threat global warming climate change. Increasing food production expected meet challenges needs that will continue increase in future. Automation one solutions productivity, including aquaculture industry, where fish recognition essential support it. This paper presents using YOLO version 4 (YOLOv4) on "Fish-Pak" dataset, which contains six species identical structurally damaged fish, both are...
Automation plays a crucial role in scaling up freshwater fish cultivation to address the future threat of food scarcity and meet growing nutrition needs. The industry, particular, develops automation sorting selection processes. However, research on this technology's development is still very limited. In work, we propose an approach for detecting classifying running conveyors. We use YOLOv8, which most popular newest deep learning model object detection classification. conducted our test...
The identification and categorization of fish is a popular fascinating research topic. Many researchers have developed expertise in detection, both underwater outside the water, which particularly beneficial for population management aquaculture. This paper proposes recognition approach using landmarking methodology with YOLO version 4 to identify categorize different backdrop circumstances. can be used on land. proposed was evaluated four distinct types from BYU dataset. final test result...
Automatic fish recognition using machine and computer vision has a vital role in developing automation the industry, which is part of food to increase productivity. It answer challenges high demand threat scarcity future due continued growth world population impact global warming climate change. And one them for automatic sorting system, some unique challenges. In this work, we created our own dataset, consisting 8 types river fish, are endemic, generally bred, sold, consumed around...
The detection and classification of fish is a prevalent fascinating area study.Numerous researchers develop skills in recognition both aquatic non-aquatic environments, which beneficial for population control the aquaculture industry, respectively.Rarely research conducted to optimize with diverse backgrounds.This paper proposes method that uses landmarking technique YOLO version 4 detect classify varying background conditions, making it applicable underwater terrestrial recognition.The...