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Pre-planting weed detection based on ground field spectral data.

Luan P Pott^1,2, Telmo Jc Amado³, Raí A Schwalbert^1,2

¹Agricultural Engineering Department, Federal University of Santa Maria, Rural Science Centre, Santa Maria, Brazil.

Pest Management Science

|October 7, 2019

Summary

Accurate spectral bands and vegetation indices effectively differentiate weeds from soil and crop residues before planting. This supports site-specific weed management (SSWM) with over 95% accuracy.

Keywords:

site-specific weed management (SSWM)spectral curves, spectral bands vegetation indices

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Area of Science:

Agricultural Science
Remote Sensing
Precision Agriculture

Background:

Site-specific weed management (SSWM) requires high-resolution data for effective weed mapping.
Optical sensor efficiency in discriminating weeds from soil and residues pre-crop establishment is crucial for SSWM success.

Purpose of the Study:

Evaluate spectral bands for differentiating weeds from non-targets.
Assess vegetation indices (VIs) to enhance weed discrimination.
Determine the accuracy of VI thresholds for distinguishing weeds before planting.

Main Methods:

Analysis of spectral bands and vegetation indices using ground-level sensor data.
Utilized training and validation datasets for accuracy assessment.
Compared spectral signatures of weeds, soils, and crop residues.

Main Results:

Red and near-infrared bands showed higher accuracy in differentiation.
Vegetation indices significantly improved discrimination compared to single bands.
Achieved over 95% overall accuracy and a kappa coefficient above 0.93.

Conclusions:

A novel approach using field spectral data distinguishes weeds from non-targets pre-planting.
Results enhance understanding of spectral signatures for SSWM.
Study limitations include spatial resolution and varying soil/residue conditions.