Project Overview

The Smart Drone project addresses challenges in apple orchard management in Himachal Pradesh, including low efficiency in plant health monitoring, costly drone solutions, and unoptimized fertilizer/pesticide application.

Problem Statement

• Commercial multispectral drones cost ₹3L+, making them inaccessible to most farmers.

• Current monitoring methods are manual, time-consuming, and prone to error.

• Fertilization and pesticide application is generalized, causing waste and environmental impact.

Process & Execution

• Conceptual Design: CAD modeling of drone structure, electronics architecture, and weight/power optimization.

• Multispectral Imaging: DIY sensors covering NIR (700–1000 nm), Red (620–750 nm), Red-Edge (700–750 nm), and Green (500–600 nm).

• Electronics & Communication: Microcontroller-based central unit with LoRa long-range modules for orchard-wide coverage.

• Deep Learning Models: Early disease detection and yield prediction trained on spectral band data.

• Variable Rate Spraying (VRT): Fertilizer and pesticide application linked to plant health maps, reducing resource usage.

Outcome & Impact

• Reduced cost by ~70–80% compared to commercial solutions.

• PDEU university-level winners; advanced to national top 50.

• Potential for early disease detection, yield prediction, and precision spraying at scale.

• Demonstrates feasibility of low-cost, scalable, farmer-friendly drone solutions.

Team & Roles

• Sumer Pandey: Microprocessors & Electronics, Project Lead

• Dhruv Rathod: Communication Systems, LoRa architecture

• Kandarp Trivedi: Electronic subsystem integration

• Kashish Sharma: Mechanical CAD design and structural modeling

• Diya: Software interface and control flow

• Jimit Chavda: Deep learning for yield prediction and disease detection

Metrics & Technical Highlights

• Plant health bands: NIR, Red, Red-Edge, Green

• Communication: LoRa modules with orchard-wide coverage

• DIY sensors reduce cost 70–80%

• Competition outcome: PDEU winners