Syed Murtaza Ahmed
- Hall Ticket: 1604-24-736-017
- Role: Principal System Architect (Core Electronics, Control Software, and ML Integration)
R&D Project Completion Report
Official Research and Development Cell Project Completion Report (2024-2025) for the Smart Agri Four Legged Bot.
Muffakham Jah College of Engineering & Technology
Section titled “Muffakham Jah College of Engineering & Technology”Department of Mechanical Engineering — Research & Development Cell
Project Approval Status: Approved
1. Project Overview
Section titled “1. Project Overview”- Project Title: Smart Agri Four Legged Bot
- Department: Mechanical Engineering Department
- Academic Year: 2024–2025
- Project Guides:
- Dr. K. Hema Latha (Associate Professor, MED)
- Dr. Uma N. Dulhare (Professor, CSE)
- Mrs. B. Sucharitha (Assistant Professor, ECE)
2. Project Team
Section titled “2. Project Team”Below is the undergraduate student team associated with the project development:
M. Zainul Abedin Kaleemi
- Hall Ticket: 1604-24-736-041
- Role: CAD & Mechanical Design, Team-Faculty Coordinator
Mohammed Qubaib
- Hall Ticket: 1604-24-735-026
- Role: Electrical System Wiring, Sensor Integration & Logistics Coordinator
Mohammed Muzzammil
- Hall Ticket: 1604-24-736-039
- Role: CAD & Mechanical Design, Assembly Layout
Syed Faisal
- Hall Ticket: 1604-24-736-029
- Role: Structural Fabrication, Assembly Operations & Logistics Coordinator
3. Project Objectives
Section titled “3. Project Objectives”The project developed a semi-autonomous agricultural robot to support precision farming through automated weed detection and soil condition monitoring:
- Reliability: Create a reliable, field-deployable robotic system capable of basic environmental sensing and visual processing.
- Practicality: Design a modular and adaptable platform that is practical to fabricate, operate, and maintain under academic constraints.
- Chassis Stability: Implement a stable mechanical structure using a mild steel chassis and 3D-printed components, ensuring durability and suitability for agricultural terrain.
- Sensor Integration: Integrate advanced sensors, including a depth camera for weed detection and a soil probe for monitoring pH, moisture, light, and fertility.
- Edge AI: Deploy a lightweight convolutional neural network for real-time weed classification and analysis.
- Power Distribution: Establish efficient power distribution and structured wiring for reliable and consistent operation in field tests.
4. Abstract
Section titled “4. Abstract”This project centers on developing a semi-autonomous agricultural robot to support precision farming through weed detection and soil condition monitoring. The goal was to design a dependable, field-ready system capable of basic sensing and visual processing, while remaining practical to build, operate, and maintain under academic limitations. The design emphasizes modularity and real-world usability, allowing future adaptability and upgrades.
Mechanically, the robot uses a mild steel chassis with four wheeled-leg assemblies, prioritizing stability, ground clearance, and simple assembly for agricultural terrain. Structural parts were modeled in SolidWorks and tested with Finite Element Analysis (FEA) to verify load capacity and vibration resistance. Custom enclosures and sensor mounts were fabricated via FDM 3D printing using PETG and PLA+, resulting in a durable yet flexible framework suited for varied field conditions.
On the electronics and software side, a Jetson Nano manages sensor integration and machine learning tasks. A RealSense D435 depth camera provides visual input, while an Amici Sense soil probe measures moisture, pH, light, and fertility. Weed detection is handled by a lightweight convolutional neural network deployed locally, with outputs logged in real time. Careful power management and wiring ensured reliable performance during testing. Together, these systems form a compact and intelligent platform capable of advancing scalable agricultural automation.
5. Methodology
Section titled “5. Methodology”The implementation path combined simulation, structural engineering, software deployment, and field trials:
- Design and Modelling: The robot’s structure was designed in SolidWorks to ensure stability, ground clearance, and suitability for agricultural terrain. Key components were analyzed through Finite Element Analysis (FEA) to validate strength, load capacity, and vibration resistance.
- Hybrid Fabrication: The chassis and load-bearing parts were built using mild steel for robustness, while custom enclosures and sensor mounts were fabricated using FDM 3D printing with PETG and PLA+ to achieve a lightweight, adaptable framework.
- Sensor Integration: A RealSense D435 depth camera was employed for visual data collection, while an Amici Sense soil probe measured parameters such as moisture, pH, fertility, and light intensity.
- Computing and Control: A Jetson Nano single-board computer managed sensor fusion, image processing, and machine learning inference. Power distribution circuits and structured wiring ensured stable operation in field conditions.
- Machine Learning & Image Processing: A lightweight Convolutional Neural Network (CNN) was trained and deployed locally on the Jetson Nano for real-time weed detection and classification. Visual data was logged for performance analysis.
6. Achievements of the Project
Section titled “6. Achievements of the Project”- Technical papers: To be Published
- Technical Reports: To be Published
- Commercial Applicability / Patent: Under evaluation
7. Details of Project Expenses (Phase 2)
Section titled “7. Details of Project Expenses (Phase 2)”- Phase 2 Seed Funds from R&D Cell: ₹55,130.40
- Phase 2 Expenditure from Seed Funds: ₹55,130.40
The following table details the project expenses incurred specifically during Phase 2 (System Optimization, Prototyping, and Field Validation):
| S.No | Date | Bill No. | Particulars of the Bills | Amount |
|---|---|---|---|---|
| 1 | 12/03/2026 | SAFL-RECEIPTS-15.png | 10-inch Wheeled Leg Hub Motors (Qty 4) | ₹13,452.00 |
| 2 | 26/03/2026 | SAFL-RECEIPTS-3.png | M4/M5 Allen Bolts, Keys, and Measurement Rule (Estimate) | ₹849.00 |
| 3 | 26/03/2026 | SAFL-RECEIPTS-11.png | M4/M5 Allen Bolts, Hand Tools (Screwdriver Set), & Fasteners | ₹1,002.00 |
| 4 | 26/03/2026 | SAFL-RECEIPTS-23.png | 36V 15.0Ah 10S3P High-Capacity NMC Lithium-Ion Battery Pack | ₹13,399.00 |
| 5 | 27/03/2026 | SAFL-RECEIPTS-22.png | Core Electronics Suite (ATmega2560 Board, Buck Converters, Voltmeters, Jumper Cables, and Acrylic Case) | ₹3,929.88 |
| 6 | 31/03/2026 | SAFL-RECEIPTS-18.png | Storage & Connectivity Suite (SanDisk Extreme 128GB microSDXC, Ambrane Braided Micro USB Cable) | ₹2,083.00 |
| 7 | 01/04/2026 | SAFL-RECEIPTS-2.png | FRC Ribbon Cables, Header Pins, and JST Connectors | ₹950.00 |
| 8 | 01/04/2026 | SAFL-RECEIPTS-7.png | Fuel (Petrol, 3.72 Liters) for Field Traversal & Prototyping Logistics | ₹400.00 |
| 9 | 01/04/2026 | SAFL-RECEIPTS-9.png | High-Current XT60/XT90 Power Connectors, Terminal Blocks, & Toggle Switches | ₹460.00 |
| 10 | 01/04/2026 | SAFL-RECEIPTS-12.png | Prototyping Tools (Digital Multimeter, Jumper Wires, & SD Card Reader) | ₹932.00 |
| 11 | 01/04/2026 | SAFL-RECEIPTS-16.png | Flexible Hook-up Wires, Ribbon Cables, & Heat-Shrink Sleeving | ₹743.00 |
| 12 | 01/04/2026 | SAFL-RECEIPTS-17.png | Tizum 6-in-1 USB-C & OTG Multi-Card Reader | ₹246.08 |
| 13 | 03/04/2026 | SAFL-RECEIPTS-13.png | DC Brushless Motor Driver Board | ₹1,298.00 |
| 14 | 03/04/2026 | SAFL-RECEIPTS-21.png | 10S 42V 6A Lithium-Ion Smart Battery Charger with XT60 | ₹3,884.00 |
| 15 | 04/04/2026 | SAFL-RECEIPTS-8.png | BTS7960 High-Power H-Bridge Motor Driver Module | ₹325.00 |
| 16 | 06/04/2026 | SAFL-RECEIPTS-20.png | RS485 Industrial Soil Sensor (Soil Moisture, Temperature, & Humidity Probes) | ₹1,746.00 |
| 17 | 07/04/2026 | SAFL-RECEIPTS-4.png | 1/18 Copper Core Hook-up Wire | ₹108.00 |
| 18 | 07/04/2026 | SAFL-RECEIPTS-5.png | 4mm Studs and Flanged Hex Nuts | ₹60.00 |
| 19 | 07/04/2026 | SAFL-RECEIPTS-10.png | ESP32 38-Pin Wi-Fi/Bluetooth NodeMCU Development Board | ₹413.00 |
| 20 | 07/04/2026 | SAFL-RECEIPTS-24.png | EvoFox Elite X2 Pro Wireless Controller for Rover Control | ₹2,404.00 |
| 21 | 08/04/2026 | SAFL-RECEIPTS-1.png | Chassis Spray Paint & Protective Surface Coating | ₹180.00 |
| 22 | 08/04/2026 | SAFL-RECEIPTS-6.png | Allen Keys, Fasteners, and Hex Nuts Hardware | ₹485.00 |
| 23 | 08/04/2026 | SAFL-RECEIPTS-14.png | Raspberry Pi Camera Module v2 (8MP, 1080p) | ₹2,360.00 |
| 24 | 08/04/2026 | SAFL-RECEIPTS-25.png | TP-Link TL-WN725N 150Mbps USB Wi-Fi Adapter (excluding personal items/beverages) | ₹503.04 |
| 25 | 16/04/2026 | SAFL-RECEIPTS-19.png | Custom 3D-Printed Parts (PLA/PETG Filament for Sensor Mounts & Shells) | ₹2,218.40 |
| 26 | SAFL-RECEIPTS-26.png | RS485 to USB Interface Serial Converter Module (Estimate) | ₹700.00 | |
| Total | ₹55,130.40 |
8. Scope for Future Work
Section titled “8. Scope for Future Work”The platform is designed to allow continuous enhancements in the following domains:
- Full Autonomy: Enhancing navigation with GPS, SLAM, and path-planning algorithms to enable completely autonomous field operations.
- Robotic Arm Integration: Adding a lightweight manipulator for tasks such as targeted weed removal, sample collection, or precision spraying.
- Expanded Sensor Suite: Incorporating advanced soil and crop health sensors (e.g., nutrient sensors, multispectral cameras) for more detailed environmental monitoring.
- Dataset Growth & AI Improvement: Training the convolutional neural network with larger, more diverse agricultural datasets to improve weed detection accuracy across different crops and regions.
- Energy Optimization: Exploring renewable power options such as solar charging and higher-capacity batteries for extended field deployment.
- Scalability & Commercialization: Refining the design for mass production with modular components, reducing cost while improving maintainability.
- Multi-Robot Collaboration: Developing swarm-based approaches where multiple units can coordinate tasks, increasing efficiency in large-scale farming.