DEVELOPING AN AI PROJECT FOR THE ABOVE PROGRAM CAN SIGNIFICANTLY ENHANCE ITS EFFICIENCY, SCALABILITY, AND IMPACT. HERE’S A COMPREHENSIVE OUTLINE FOR AN AI PROJECT TAILORED TO THE FISH/MEAT DRYING AND HYDRATION SYSTEMS: PART 3

Microcontroller

Raspberry
Pi 4 Model B

Data
collection, processing, communication

Supports
Linux OS, powerful enough for ML inference

Microcontroller

Arduino
Uno or Mega

Sensor
interfacing

Suitable
for simple sensor data collection

Camera

Raspberry Pi Camera Module V2

Visual inspection of products

High resolution, compatible with Raspberry Pi

Lighting

LED ring lights

Consistent illumination for imaging

Reduces shadows and glare

Cloud Service

AWS IoT Core + S3 / DynamoDB

Data ingestion and storage

Scalable, reliable

Local Server

NAS or dedicated server

Backup and local processing

Optional for offline scenarios

TensorFlow

Latest stable release

Deep learning models

Image classification, process optimization

Keras

Included with TensorFlow

Simplified model building

Prototype CNNs for image analysis

scikit-learn

Latest version

Classical ML models

Predictive analytics, regression

OpenCV

Image pre-processing

Enhance images, detect defects

TensorFlow Object Detection API

Automated defect detection

Pre-trained models fine-tuned for product
inspection

CNN Architectures

ResNet, MobileNet

Efficient models for embedded deployment

PLC

Siemens S7-1200 or Allen-Bradley CompactLogix

Automated process control

Integrate with sensors and AI outputs

MQTT Broker

Mosquitto

Messaging protocol

Lightweight, ideal for IoT communication

Actuators

Servo motors, relays

Adjust drying/hydrating parameters

Controlled via PLC or microcontrollers

Dashboard

Grafana or Power BI

Real-time monitoring

Connects to cloud databases

Web App

React.js or Angular

Remote system control

User-friendly interface

MQTT

Device communication

Low latency, efficient

REST API

Data exchange

Between AI models and control systems