2021 Aquaculture Artificial Intelligence Idea Contest

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

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.

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