Agri-AI

Agri-AI is an AI-driven toolkit for smart agriculture maintained by SLASH MNT.

This repository contains machine learning models, inference services, and utilities designed to help build intelligent agriculture solutions, such as crop health analysis, disease detection, and predictive insights. It is intended for engineers and practitioners who want to integrate AI into cloud-native, Kubernetes-based, or edge computing environments.

Overview

Modern agriculture increasingly relies on data and automation to improve yields, reduce waste, and optimize resource usage.
Agri-AI aims to provide reusable, production-oriented AI components that can be embedded into larger systems.

The project focuses on:

• Practical AI use cases for agriculture
• Reusable and extensible model pipelines
• Deployment-ready services (containerized, cloud-native)
• Clear separation between models, inference, and integration layers

Features

Depending on the components enabled or added, Agri-AI can include:

• Crop health and disease detection from images
• Plant stress or nutrient deficiency classification
• Yield or growth prediction models
• Batch and real-time inference pipelines
• APIs or services for integration with dashboards and control systems

The repository is designed to be accessible to:

• Kubernetes engineers
• Data scientists
• Agritech developers
• Platform and DevOps teams

Prerequisites

Before getting started, ensure you have:

• Python 3.8+
• Docker (optional, for containerized usage)
• Kubernetes (optional, for deployment)
• Access to required model weights or datasets

Getting Started

Clone the repository

git clone https://github.com/slash-mnt/agri-ai.git
cd agri-ai

Install dependencies

pip install -r requirements.txt

Usage

Local inference example

Run an inference script locally:

python app.py --input /path/to/input.jpg

The input can be an image or dataset supported by the model.
The output typically includes predicted labels, scores, or classifications related to crop health or condition.

Container usage

Build the container image:

podman build -t slashmnt/agri-ai:latest .

Run the container:

podman run -p 8000:8000 slashmnt/agri-ai:latest

Deployment

Agri-AI components can be deployed as:

• Standalone containers
• Kubernetes Deployments or Jobs
• Inference services behind an API gateway
• Edge workloads close to sensors or cameras

The architecture allows integration with:

• CI/CD pipelines
• GitOps workflows
• Observability stacks
• Data ingestion systems

Models and Data

This repository may include or reference:

• Image-based models (e.g. CNNs)
• Tabular or time-series models
• Frameworks such as PyTorch, TensorFlow, or ONNX

Model weights may be stored locally, downloaded during build, or mounted at runtime.

Contributing

Contributions are welcome.

To contribute:

1. Fork the repository
2. Create a feature or fix branch
3. Add tests and documentation where applicable
4. Open a pull request with a clear description

Please ensure new components are documented and follow existing structure and conventions.

Security Considerations

• Validate inputs before inference
• Do not commit sensitive data or credentials
• Use secure container images for production
• Prefer secrets management solutions for keys and tokens

License

This project is licensed under the MIT License.
See the LICENSE file for details.