2021 Aquaculture Artificial Intelligence Idea Contest

[Project Page] [Challenge Page] [Demo Program] [Presentation]

Table of Contents

• Overview
• Team information
• Our research
• Setup project
• How to run program
• Citation

Overview

• Subject: Free Topic (Food-organism utilization throughout the AI-based aquaculture industry)
• Home page: http://sarc.jnu.ac.kr/contest/20211105/
• Motivation:
  • Stable mass feeding management of food organisms is importance of 'artificial seed culture' industry
  • Problems cause serious economic loss in the aquaculture artificial seed production industry
    • Reduction of aquaculture food organisms (plankton)
    • Difficulty in managing mass culture/feeding of food organisms
    • Mass mortality in the process of seed production
    • Decreased utilization of food organisms by field

    A sharp decline in marine food organisms (plankton) due to global warming and marine environmental pollution

  • It is necessary to select and concentrate the government R&D AI data and technology to solve fundamental problems such as instability and low productivity in the aquaculture industry

Team information:

• Team Name: ADLER
• Affiliation: Chonnam National University, South Korea

Our research

• Please see in our project page at [Project Page]

Setup Project

Project Structure:

project
├── aquaculture
│   ├── app_v2
│   ├── apps
│   ├── assets
│   │   ├── cache
│   │   ├── data <-- Setup Data step
│   │   │   └── final_info.csv 
│   │   └── models
│   ├── exps
│   ├── utils
│   ├── cli_main.py
│   └── common.py
│   └── ...
├── data
│   ├── a2i_data <-- copy csv, 먹이생물 into here
│   │   ├── csv
│   │   │   ├── 10월01일
│   │   │   │   ├── 2-1-1-1-1-1001-0010000.csv (id-code.csv - sensors data)
│   │   │   │   └── 2-1-1-1-1-1001-0020000.csv
│   │   │   ├── 10월04일
│   │   │   └── ...
│   │   └── 먹이생물
│   │       ├── 10월01일
│   │       │   ├── 고성
│   │       │   │   ├── 2-1-1-2-2-1001-0120001.jpg (id-code.jpg - microsopy images)
│   │       │   │   ├── 2-1-1-2-2-1001-0120002.jpg
│   │       │   │   └── ...
│   │       │   ├── 일해
│   │       │   │   ├── 2-1-1-2-2-1001-0110001.jpg
│   │       │   │   ├── 2-1-1-2-2-1001-0110002.jpg
│   │       │   │   └── ...
│   │       ├── 10월04일
│   │       └── ...
│   ├── preprocessed <-- Setup Data step
│   │   ├── full_info.hdf5
│   │   ├── full_info.xlsx
│   │   ├── final_info.csv
│   │   ├── final_info.xlsx
│   │   └── final_info.hdf5
│   └── exps
└── images

Setup Environments

• Install Anaconda3 at https://www.anaconda.com/products/individual
• Activate environment base

# Linux
conda activate base

# Window
activate base

• Create environment a2i

conda create -n a2i python=3.8

• Activate environment a2i

# Linux
conda activate a2i

# Window
activate a2i

• Install requirements packages at environment base

pip install -r requirements.txt 

Setup Data

• Copy csv (sensors data), 먹이생물 (microscopy images) to folder data
• Open console
• Go to project root

# Linux
cd <project dir>

# Window
cd /d <project dir>

• Activate Environment a2i

# Linux
conda activate a2i

# Window
activate a2i

• Generate index files

python aquaculture/cli_main.py index
python aquaculture/cli_main.py detect-all 

How to run program

Run application

• Go to project root
• Activate Environment a2i
• Type command

python aquaculture/cli_main.py app2 --app-type dash

• Open Web browser and type url: http://localhost:8050

Run console tasks

• Go to project root
• Activate Environment a2i
• Type commands
  • Generate index files

python aquaculture/cli_main.py index

• Detect number of cells in a microscopy image

python aquaculture/cli_main.py detect-one --id-code <id_code> (2-1-1-2-2-1001-0120126)

• Detect number of cells in all microscopy image and save to index file

python aquaculture/cli_main.py detect-all

• Data analysis belongs to places, grouping by day

python aquaculture/cli_main.py data-analysis

• Training and Evaluating baseline algorithms

python aquaculture/cli_main.py baseline --config <config file>

• Training and Evaluating tabnet algorithms

python aquaculture/cli_main.py tabnet --config <config file>

• Prediction food-organism quality from sensor data

python aquaculture/cli_main.py prediction 
  --model <model_name> (sklearn, tabnet)
  --model-path <the path of model weights>
  --id-code <id_code>

or

python aquaculture/cli_main.py prediction 
  --model <model_name> (sklearn, tabnet)
  --model-path <the path of model weights>
  --temp <temparature> 
  --do <dissolved oxygen> 
  --ph <pH>
  --sal <salinity>
  --ntu <nephelometric turbidity unit> 

View Experiment Demo

• Open html files in notebooks folder to view results of console tasks

Citation

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

All material is made available under Creative Commons BY-NC-SA 4.0 license by Adobe Inc. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicating any changes that you've made.