Farmer Eye Robotic Car
Edge AI system for real-time plant disease detection and autonomous field monitoring using computer vision and IoT.
King Salman International University · B.Sc. Computer Science AI · 2025
The Problem
- • Manual plant health monitoring is time-consuming and prone to human error.
- • Inefficient use of resources (water, fertilizers) due to lack of real-time data.
- • Critical factors like soil temperature and wind often remain undetected.
- • Delayed identification of diseases leads to significant crop yield losses.
- • Absence of data-driven tools limits farmers' ability to make timely decisions.
The Solution
Farmer Eye is an AI-driven autonomous vehicle ecosystem. It navigates farm fields collecting real-time data through high-resolution cameras and sensors. It analyzes plant health metrics using Machine Learning to detect diseases, assess pest infestations, and provide actionable insights via a user-friendly mobile app, ensuring sustainable and precise agriculture.
A cohesive integration of Robotics, AI, IoT, and Mobile Software.
1. Smart Vehicle (Robotics)
Chassis & Mobility: Custom-built RC car chassis with high-torque motors driven by an Arduino microcontroller.
Sensors & GPS: Equipped with DHT sensors for detecting temperature/humidity and a GPS module for precise geolocation of every scanned plant.
Sustainability: Integrated Solar Panels provide a reliable, clean energy source to charge batteries and extend operation.
2. AI & Cognitive Core
Inference Optimization: Converted the model to TensorFlow Lite to enable low-latency, efficient real-time inference on the Raspberry Pi 4 edge device.
Vision System: 8MP Camera Module captures high-definition images of crops for analysis.
Deep Learning Model: Leveraged Transfer Learning by fine-tuning pre-trained CNN architectures on the PlantVillage Dataset.
→ Accuracy: ~94%
→ Loss Function: categorical_crossentropy (38 Classes)
3. Mobile Command Center
Flutter App: A cross-platform app providing farmers with diagnostic reports, disease location maps, and treatment recommendations.
Real-time IoT: Seamlessly connected to the vehicle via APIs and Firebase/Firestore for live monitoring and history tracking.
4. Data Engineering
Preprocessing Deep Dive: Applied rigorous Normalization (rescaling pixel values to 0-1) and resizing to fixed 224x224 dimensions for consistency.
Advanced Augmentation: Employed strategies like Rotation, Shear, Zoom, & Shift to synthetically expand the dataset and improve model generalization.
Dataset: Utilized 54,000+ labeled images covering healthy and diseased states of crops like Tomato, Potato, and Apple.
I spearheaded the development of the system's cognitive core, acting as the AI Architect. My primary focus was constructing the 'brain' of the rover:
- •Model Architecture Design: Designed and implemented specific Deep Learning architectures (CNNs) using Keras and TensorFlow to maximize detection accuracy on edge devices.
- •Edge Optimization: Optimized heavy neural networks to run efficiently on the Raspberry Pi 4, ensuring low-latency inference without internet dependency.
- •Data Engineering Pipeline: Engineered robust data pipelines for preprocessing real-time camera feeds and handling the massive PlantVillage dataset for training.
High Accuracy
Achieved 94% classification accuracy on the PlantVillage dataset using fine-tuned CNNs.
Real-Time Inference
Optimized performance on Raspberry Pi with inference times of ~200ms per image.
Comprehensive Coverage
Supports detection of 38 distinct plant disease classes across multiple crop species.
Efficiency Boost
Reduced manual crop inspection time by approximately 70% through automation.
• Edge Deployment ConstraintsRunning complex CNNs on a Raspberry Pi 4 was computationally expensive. Solution: We utilized TensorFlow Lite quantization to reduce model size by 75% without significant accuracy loss.
• Real-time Inference LagBalancing frame rate with detection accuracy was critical. Solution: Optimized the inference pipeline to process frames at ~5 FPS for a slow-moving agricultural rover.
• Handling Class ImbalanceThe dataset had unequal samples for some disease classes. Solution: Applied advanced data augmentation (Shear, Zoom) and class weighting during training.
• IoT + AI IntegrationSynchronizing the rover's movement with cloud data streaming was complex. Solution: Architected an asynchronous event loop using Python's asyncio to handle motor control and Firebase updates concurrently.
KING SALMAN INTERNATIONAL UNIVERSITY — OFFICIAL NEWS
Outstanding Grant Awardees — ASRT Research Funding
The university officially recognized this project for reaching the finals of the ASRT (Academy of Scientific Research & Technology) competition, winning a research grant for the project "Smart Vehicle for Plant Disease Detection Using AI and IoT".
Read official announcementDR. SAEED MOHSEN — PROJECT SUPERVISOR
"As a graduation project supervisor at KSIU University —Smart Vehicle for Plant Disease Detection and Classification Using AI and IoT (Farmer Eye Robotic Car). Thanks to my best team and best wishes."
View on LinkedInASRT Funding
Secured funding from the Academy of Scientific Research and Technology to support system development.
Innovation Exhibition
Participated in the 3rd International Youth AI Forum showcasing agricultural innovations.
ITAC Grant
Awarded funding from ITAC to advance technical research and optimize the intelligent model.