You will get AI-IoT Smart Farming System for Crop Monitoring & Irrigation
Rising Talent
Project details
Most AI agriculture projects deliver a model and stop there. This project delivers a complete, tested system trained YOLO-based nutrient stress detection, automated IoT irrigation control, real-time mobile alerts, and a Flask backend, all validated end-to-end on real wheat crop data.
The AI module detects nutrient stress across 10 deficiency classes, trained on 5,000 annotated wheat leaf images with full augmentation. The system does not just detect problems it triggers IoT-connected tubewells and hydraulic gates automatically, closing the loop between diagnosis and action.
What sets this apart is practical depth. The architecture has been unit tested, integration tested, and black-box validated as a working system, not a prototype or notebook demo. It also scales: the same validated AI and IoT core extends to UAV and UGV-based mobile sensing for large fields, without retraining or rebuilding from scratch.
Whether you need a standalone deficiency detection model, a full AI-IoT farming system, or a mobile robotic sensing extension for wide-scale fields, each tier is scoped to deliver production-ready outputs with source code, documentation, and a test report included.
The AI module detects nutrient stress across 10 deficiency classes, trained on 5,000 annotated wheat leaf images with full augmentation. The system does not just detect problems it triggers IoT-connected tubewells and hydraulic gates automatically, closing the loop between diagnosis and action.
What sets this apart is practical depth. The architecture has been unit tested, integration tested, and black-box validated as a working system, not a prototype or notebook demo. It also scales: the same validated AI and IoT core extends to UAV and UGV-based mobile sensing for large fields, without retraining or rebuilding from scratch.
Whether you need a standalone deficiency detection model, a full AI-IoT farming system, or a mobile robotic sensing extension for wide-scale fields, each tier is scoped to deliver production-ready outputs with source code, documentation, and a test report included.
Machine Learning Tools
NLTK, NumPy, OpenCV, Python Scikit-Learn, PyTorch, SciPy, TensorFlow, Word2vecWhat's included
| Service Tiers |
Starter
$250
|
Standard
$650
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 2 | 4 | 6 |
Number of Graphs/Charts | 3 | 6 | 9 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
About Hafsa
AI Engineer | Machine Learning | Deep Learning | NLP | Computer Vision
Rawalpindi, Pakistan - 1:21 am local time
I have worked on AI-driven solutions including voice bots, recommendation systems, sign language recognition, medical AI systems, football player detection, sentiment analysis, and automation tools. My experience includes developing and deploying ML models, integrating REST APIs, building data pipelines, and creating AI-powered web and mobile applications.
I am skilled in Python, TensorFlow, PyTorch, OpenCV, Scikit-learn, Flask, Node.js, Docker, PostgreSQL, and modern AI frameworks. Alongside AI development, I also build responsive Flutter mobile applications and full-stack web solutions.
I focus on delivering:
Clean and scalable code
High-performance AI models
Production-ready backend systems
API integration and deployment
Reliable communication and on-time delivery
Whether you need an AI chatbot, NLP solution, computer vision system, automation tool, ML model, or full-stack AI application, I can help turn your idea into a working product.
Steps for completing your project
After purchasing the project, send requirements so Hafsa can start the project.
Delivery time starts when Hafsa receives requirements from you.
Hafsa works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Review & Scoping
Analyse client inputs, confirm crop type, deficiency classes, hardware stack, and field scale. Deliver a scoping brief within 24 hours of kickoff.
Dataset Preparation & Augmentation
Collect or validate the leaf image dataset, apply augmentation (rotation, flips, brightness shifts), and split into 70/15/15 train-val-test sets