Promising Projects

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Project Title: Smart and sustainable wheat fields in Iraq using (AgriGuard AI) hybrid Robot and Drone

Description:
حقول الحنطة الذكية والمستدامة في العراق بأستخدام نظام الروبوت والطائرة المسيرة الذكي (AgriGuard AI)
يتناول هذا المشروع مشكلةً جوهريةً في مجال الزراعة الدقيقة والاستدامة البيئية. ويستند الحل المقترح إلى نظام روبوتي هجين متكامل )طائرة مسيرة وروبوت أرضي( يعتمد على حوسبة الحافة، ودمج مستشعرات متطورة تنغرس فيزيائيا للفحص المباشر لعمق التربة، والخرائط الجوية الذكية
باستخدام YOLOv8-nano ، المربوطة بتطبيق أندرويد منخفض زمن الاستجابة. يهدف هذا المشروع بشكل رئيسي إلى تأسيس منظومة تحكم ذكية مغلقة تقوم بتحليل بيانات الرطوبة والملوحة الناتجة عن غرس الحساسات لتحديد وإضافة المقدار الدقيق الذي يحتاجه النبات من مياه أو أسمدة أو معالجات كيميائية مستهدفة. وتشمل النتائج المتوقعة خفضا كبيراً في هدر الموارد وتقليل استخدام المواد الكيمياوية مما يقلل التأثيرات البيئية، واستخراج قراءات مباشرة وعالية الدقة لشبكة الجذور الكيميائية عبر ناقل Modbus RS485 بنسبة خطأ أقل من 2%، وتحقيق مرونة تشغيلية عالية للأنظمة تحت درجات حرارة ميدانية تتجاوز 50 درجة مئوية. اثبتت النتائج المستحصلة امكانية التطبيق الصناعي في العراق لتحقيق الأمن الغذائي المستدام بتكنولوجيا الذكاء الأصطناعي الصديق للبيئة وبأقل التكاليف .

Uploaded At: 2026-07-04 19:35:34

Project Title: Design and Construction of a Multifunctional Lawn Mower System

Description:
This project presents the design, implementation, and evaluation of a heavy-duty multifunctional lawn mower system with remote control capabilities. The system combines a custom-fabricated octagonal steel chassis, four independently driven high-torque DC gear motors with planetary gearboxes, pneumatic off-road tires, and a 25.6 V / 100 Ah LiFePO4 battery pack, yielding reliable locomotion across grass, gravel, uneven soil, and moderate inclines. Control is implemented through a six-channel 2.4 GHz RF transmitter–receiver pair, allowing the operator independent management of drive direction, drive speed, blade activation, and emergency shut-off. Field testing demonstrated a top speed of 1.2 m/s on flat grass, an RF communication range of approximately 120 m in open field, a continuous runtime of 4.6 h under typical mowing load, and a mean cutting efficiency of 92 % across the test lawn. The proposed system offers a cost-effective and mechanically robust solution for residential and commercial lawn maintenance, particularly suited to the rough terrain and hot climate of the Middle East.

Uploaded At: 2026-07-05 12:44:28

Project Title: Sanabel A Smart System for Plant-Health Monitoring with Artificial Intelligence

Description:
Sanabel is an integrated smart AgriTech platform for monitoring corn crop health in Iraqi agricultural fields, combining UAV technology, artificial intelligence, and IoT-based sensing into a unified system.
The platform uses a Parrot ANAFI 4K drone to capture high-resolution aerial imagery of the field, which is analyzed through a YOLOv8-based AI model for early detection of corn diseases such as Northern Leaf Blight, Maize Streak Virus, and Gray Leaf Spot. The system integrates solar-powered ground stations equipped with RS485 soil sensors that measure humidity, temperature, and nutrient levels (NPK), communicating wirelessly via LoRa to a central Raspberry Pi 5 processing unit. Aerial imagery is further processed using WebODM to generate orthophoto maps and NDVI-based vegetation health analysis.
Tested on a real corn field at the University of Baghdad, Sanabel achieved an 89.4% photogrammetric reconstruction success rate and identified Northern Leaf Blight as the dominant disease with 88.0% average detection confidence across the monitored field. The project aims to empower farmers with early disease detection, optimize water usage, and increase crop productivity, offering a practical and sustainable solution suited to Iraq's challenging environmental conditions.

Uploaded At: 2026-07-05 22:43:28

Project Title: MyAttendanceIQ

Description:
An AI-Powered Face Recognition System for Smart Attendance Management in Educational Institutions

Uploaded At: 2026-07-05 23:46:19

Project Title: Smart Greenhouse

Description:
This project addresses the difficulty of maintaining suitable growing conditions in large greenhouse areas while
reducing the wiring complexity and measurement inaccuracy found in conventional designs. The proposed
solution is based on a distributed network of ESP microcontrollers that communicate wirelessly with a central
ESP32-S3 controller using ESP-NOW. The system monitors inside and outside temperature, humidity, natural
light, water level, and three soil-moisture points, then controls intake and exhaust fans, water cooling, irrigation,
and supplementary purple grow lighting. The expected outcome is a scalable, low-cost greenhouse control
system that improves plant quality, reduces manual monitoring, and supports fruit and vegetable cultivation
across different seasons.

Uploaded At: 2026-07-06 02:10:52

Project Title: LeafGuard AI: A Deep Learning System Based on ResNet50 and Large Language Models for Real-Time Plant Disease Detection and Bilingual Treatment Recommendation

Description:
Plant diseases represent a major threat to agricultural productivity and food security, especially in developing regions where access to agricultural experts is limited. This project presents LeafGuard AI, an intelligent deep learning-based system for real-time plant disease detection and bilingual treatment recommendation. The proposed system combines image classification using a custom-trained ResNet50 Convolutional Neural Network (CNN), cloud-based inference using FastAPI, and dynamic treatment generation using the Google Gemini Large Language Model (LLM). The model was trained and evaluated using the CCMT Dataset-Augmented, which includes images from four crop species: cashew, cassava, maize, and tomato. After collecting the four crop folders and merging duplicated class labels, the final dataset contained 105,252 images distributed across 18 disease, pest, and healthy classes. Images were resized to 224 × 224 pixels and augmented using random flipping, rotation, zooming, and translation to improve generalization. The ResNet50 model was used through transfer learning with a custom classification head consisting of GlobalAveragePooling2D, Dropout, and a Softmax output layer. After disease detection, the system invokes the Google Gemini API to generate structured treatment recommendations in both Arabic and English. These recommendations are cached locally using AsyncStorage to reduce repeated API calls and improve response time. The proposed system provides a practical end-to-end solution for precision agriculture by integrating plant disease classification, cloud deployment, multilingual treatment guidance, and cross-platform accessibility.

Uploaded At: 2026-07-05 07:48:47

Project Title: AviGuard_NURAI

Description:
This work addresses the early detection of disease and environmental stress in poultry flocks, a central problem in precision livestock farming. Flock health can deteriorate within hours, yet manual inspection is intermittent, leaving the earliest indicators — animal behaviour, vocalisation, and air quality — unobserved between checks. We present AviGuard AI, a multi-modal monitoring system that continuously analyses three independent signal streams and fuses them, through a late-fusion neural network, into a single interpretable flock-risk score. Vision is handled by a YOLOv8 detector and a MobileNetV3-Small behaviour classifier; audio by a ResNet18-based distress classifier paired with a convolutional autoencoder for unsupervised anomaly detection; and the barn environment by a two-headed LSTM that performs both short-horizon anomaly detection and a 30-minute forecast over five sensors. A four-layer alert-verification funnel converts the fused score into a trustworthy notification by enforcing temporal persistence, cross-modal corroboration, per-barn baselining, and de-duplication. The complete system is engineered for low-cost edge hardware (NVIDIA Jetson / Raspberry Pi) with ONNX-exported models and a self-hosted FastAPI and SQLite backend serving a real-time React console. The current implementation is a working prototype validated end-to-end on synthetic and mock data, with eighteen passing integration tests; the architecture is explicitly designed so that retraining the modality models on real field data requires no changes to the surrounding system. We discuss the technical design of each model, the deployment stack, and the system's applicability across commercial and smallholder poultry production.

Uploaded At: 2026-06-30 16:42:34

Project Title: EnviroLink: AI-Based Wireless Monitoring & Control System

Description:
إنفايرولنك هو نظام ذكي لاسلكي يعمل بالطاقة الشمسية لمراقبة الزراعة والتحكم بالري. يجمع النموذج الأولي بين عقد ESP32، واتصال LoRa، ونظام ري ذكي، ومراقبة الطاقة الشمسية، ومراقبة خلايا النحل، ولوحة تحكم ويب، وتحليل صور الحقل الملتقطة بواسطة طائرة مسيرة. يستخدم النظام محرك قرار ذكي قابل للتفسير يعتمد على قواعد وأوزان لدعم قرار الري اعتمادًا على قراءات الحساسات والصور الجوية عند توفرها. أظهرت الاختبارات الوظيفية نجاح النظام في الاستشعار، الاتصال، عرض البيانات، التحكم بالمضخة، مراقبة الطاقة، ودعم اتخاذ القرار.

Uploaded At: 2026-07-05 07:47:19

Project Title: Palm-ID AI: A UAV-Assisted IoT Framework for Bias-Reduced Date Palm Cultivar Identification

Description:
Artificial intelligence (AI) has become an essential technology for precision agriculture; however, deep learning models often suffer from architecture-specific bias, in which predictions rely on irrelevant background features rather than meaningful object characteristics. This limitation is particularly evident in UAV-acquired orchard images, which exhibit significant variations in illumination, viewing angle, altitude, and environmental conditions. Such variability may reduce model robustness, explainability, and generalization.
This project presents PalmID AI, a novel UAV-assisted Internet of Things (IoT) application for automatically identifying date palm cultivars with reduced AI bias. The proposed framework integrates feature-level fusion between Xception and InceptionResNetV2, enhanced by the Convolutional Block Attention Module (CBAM). Rather than relying on a single convolutional neural network, complementary deep representations extracted from both architectures are fused to suppress architecture-specific bias while improving discriminative feature learning. High-resolution UAV images are processed using the proposed framework, and the resulting fused feature vectors are evaluated with multiple machine learning classifiers. Furthermore, Grad-CAM++ visualization is employed to verify that the proposed model consistently focuses on biologically relevant fruit clusters while minimizing attention to irrelevant background regions.
The proposed approach is expected to improve classification accuracy, robustness, explainability, and fairness while reducing prediction variability across different imaging conditions. Consequently, PalmID AI provides a trustworthy AI framework suitable for real-time precision agriculture and intelligent orchard management.

Uploaded At: 2026-07-03 03:57:31

Project Title: Spelt Grain Quality Detection Robot in Silo

Description:
Our project is titled "Intelligent Robotic System for Wheat Grain Quality Inspection in Silos." The main objective is to automate the inspection of wheat grains using computer vision and artificial intelligence, replacing the traditional laboratory inspection process with a faster and more efficient solution.

The proposed system consists of two main parts. The first part is the robotic vehicle, which moves inside the silo and captures images of wheat grains using an onboard camera. The second part is the ground station, where the captured images are received and processed using a TensorFlow-based artificial intelligence model to classify grain quality according to predefined standards. Finally, the inspection results are displayed to the user in real time.

This approach reduces inspection time, improves classification accuracy, minimizes human error, and supports faster decision-making. Overall, the system helps improve grain quality management, reduce post-harvest losses, and support smart agriculture.

Uploaded At: 2026-07-06 03:47:46