This sequence follows an applied computer vision thesis from the first failure at a new flight altitude through dataset repair, model comparison, physical canopy measurement, and a practical deployment framework.
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01
The Drone That Couldn't See: Why Altitude Breaks Your AI Model
A model trained at 3cm resolution failed at 6cm. Same drone. Same plantation. Different altitude. Here's the physics and math behind why — and what to do about it.
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02
Building the Dataset Nobody Had: Annotation, Corruption, and the Split That Kept Everything Honest
How I built a multi-resolution oil palm segmentation dataset from scratch — including the 301 corrupted label files I had to fix before anything could run.
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03
Six Models Enter, One Problem Wins: A Head-to-Head Segmentation Showdown
I trained YOLOv8, YOLOv11, Mask R-CNN, and two SAM hybrid pipelines on oil palm drone data. Here's what the numbers actually showed — including the result that surprised me most.
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04
The Hybrid Paradox: Why SAM + YOLO Sounds Better Than It Is
SAM 2.1 was trained on 1 billion masks. My 56MB YOLO model still outperformed the hybrid pipeline in most conditions. Here's the detailed explanation.
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05
From Pixels to Meters: The Math Behind Canopy Size Estimation
A segmentation mask is a collection of pixels. Here's how we convert that into a physical canopy area in square metres and an equivalent diameter — with the full derivation and error analysis.
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06
A Framework for Flying Smarter: The Practical Deployment Guide
After 8 GSD levels, 6 models, and 3,368 tile-label pairs, here's the condensed operational guide for deploying AI-based oil palm monitoring with a drone.