AI/ML Powered Precision Reforestation using Drone with Hydrogel-Coated Seed Pods

Abstract

Wildfire-affected landscapes can be difficult to restore because damaged terrain may be remote, steep, hazardous, and spatially difficult to access. This paper presents a low-cost prototype that integrates a quadcopter drone, onboard/near-edge computer vision, and a servo-controlled seed dispenser for targeted reforestation. The system was developed through eight iterative prototypes spanning flight stabilization, camera integration, seed-dispenser geometry, machine-learning classification, and hydrogel seed-pod testing. A balanced image dataset of 1,520 forest images (760 burned and 760 green) was labeled and divided into 70% training, 15% validation, and 15% test subsets. Color/texture features and a support vector machine (SVM) classifier produced 93.0% test accuracy, with 105/114 green and 107/114 burned test images correctly classified. The final electromechanical prototype demonstrated image-triggered seed release over a brown/burned target. In a separate 10-day simulated degraded-soil experiment, hydrogel-coated seeds reached 90% germination and 6.5 cm mean height, compared with 60% germination and 3.8 cm for uncoated controls. These results establish a proof of concept for coupling real-time terrain classification directly to precision seed deployment.

Introduction

The text presents an AI-guided drone system for targeted post-wildfire reforestation. The main problem addressed is that conventional aerial seeding can waste seeds by distributing them uniformly, including over healthy or unsuitable areas. The proposed system instead uses real-time aerial image classification to identify burned or degraded terrain and automatically release seeds only in those locations.

The prototype integrates five main components:

• A quadcopter drone and flight controller

• A camera for terrain imaging

• An AI/ML system that classifies terrain as green or burned

• A servo-controlled seed dispenser

• Hydrogel-coated seed pods designed to retain moisture

The project was developed through eight iterative prototypes. Improvements included adding propeller guards and protective canopies, calibrating sensors to improve flight stability, and redesigning the dispenser mechanism to achieve reliable seed release during flight.

For computer vision, the study used 1,520 forest images, evenly divided between burned and green forest. The data were split into 70% training, 15% validation, and 15% testing. An initial RGB/HSV color-thresholding approach was replaced by a machine-learning approach because simple color thresholds were sensitive to lighting and mixed terrain. The final reported classifier was a Support Vector Machine (SVM) using visual color and texture features.

On the 228-image test set, the SVM correctly classified:

• 105 of 114 green images

• 107 of 114 burned images

This resulted in an overall 93.0% accuracy, with 92.1% recall for green terrain and 93.9% recall for burned terrain.

Conclusion

The present study demonstrates an integrated approach to precision reforestation in which aerial sensing directly controls seed deployment. Across eight prototypes, the present study progressed from a camera-equipped quadcopter to a stabilized platform with a custom servo dispenser, an SVM burned/green classifier, and hydrogel seed pods. The reported classifier achieved 93.0% accuracy on a balanced 228-image test set. The integrated prototype released seeds when a burned/brown target was recognized, demonstrating the intended detection-to-action loop. In the small simulated degraded-soil experiment, hydrogel-coated seeds showed 90% germination and 6.5 cm mean height at Day 10, compared with 60% and 3.8 cm for uncoated controls. These results establish a proof of technical concept and feasibility for coupling real-time terrain classification directly to precision seed deployment of hydrogel-coated seed pods for AI-guided drone reforestation.

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Copyright

Copyright © 2026 Ayan Bera. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.