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LiDAR-Based Plant Phenotyping for Precision Agriculture

LiDAR-Based Plant Phenotyping for Precision Agriculture

This project tackles one of the biggest bottlenecks in modern crop science: how to measure plant traits quickly, accurately, and at scale. Traditional phenotyping is still largely manual. Researchers take rulers and calipers into fields, a process that is slow, destructive, and vulnerable to human error. This project proposes a smarter alternative by harnessing LiDAR sensors to capture rich 3D representations of plants and applying deep learning models to directly interpret that structural data. Instead of reducing plants to simplistic measurements like height or canopy width, the system learns to estimate key biological traits such as above-ground biomass, canopy volume, and other indicators of crop performance. By developing a full software pipeline and integrating both open datasets and custom LiDAR scans, this project aims to create a scalable, non-destructive tool that makes high-throughput phenotyping practical for breeders and agronomists.

The technical backbone of the project is a novel deep learning model, nicknamed AgriFormer, designed to predict above-ground biomass directly from 3D point cloud data. The architecture processes raw LiDAR scans through a multiscale encoder-decoder and then reconstructs dense plant structures to deliver highly accurate predictions. Crucially, this avoids the need for hand-crafted features or voxelization, which limit flexibility and accuracy. Early experiments using open-source datasets show promising results with lower error rates compared to conventional approaches. Alongside this, we build an in-house dataset using custom 2D LiDAR hardware mounted on a UAV platform, generating full 3D reconstructions of target crops.

The significance of this project lies in its potential to reshape how agricultural research is conducted. By lowering the cost and effort of high-fidelity phenotyping, it enables crop scientists to study larger populations with greater accuracy, accelerating breeding programs aimed at higher yields and climate resilience. The methodology also lowers the barrier to entry for labs with limited resources, since the conversion of 2D LiDAR scans into 3D point clouds offers a cost-effective alternative to expensive commercial systems. The pipeline and datasets produced in this work can power a new wave of data-driven agriculture, helping farmers and researchers make smarter decisions about crop management and varietal selection.

Faculty

Students

  • Abdul Wahab
  • Faareh Ahmed
  • Malik Shahzaib Khan

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