RESEARCH PROJECT

Recognition of weed classes from hypespectral images of wheat fields¶

A talk to the Department of Remote Sensing - Warsaw University of Life Sciences

Dr. Anderson Rodrigo da Silva - Goiano Federal Institute, Brazil

Sept. 3, 2021

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Installing collected packages: RISE
Successfully installed RISE-5.7.1
Note: you may need to restart the kernel to use updated packages.

Problematization¶

Weed infestation in a cotton field. Source: Bogiani (Embrapa), 2017

Infestation of Ipomoea sp. in a sugarcane field. Source: Hirata (Campos e Negócios), 2016

How are we controlling weeds?¶

Source: Comas (Embrapa), 2016

Weed scouting in a sugarcane field. Source: Christoffoleti, 2019 (https://www.grupocultivar.com.br/)

Herbicide resistance. Source: https://www.upherb.com.br/

Okay! But what about Poland?¶

  • 26 cases of weed resistance to herbicides
  • 12 on winter wheat!

Source: Heap, 2021 (http://www.weedscience.org/)

And what should we do about it?¶

Adapted from Shawn, 2005 (Front. Ecol. Environ.)

And to automatically discriminate monocots and dicots:¶

Source: Da Silva, 2019 (J. Appl. Remote Sens.)

The main objective¶

To detect and discriminate grassy and broadleaf weeds using UAV hyperspectral images of winter wheat fields.

Methodology¶

Field plots¶

Reflectance conversion¶

$R = \frac{V_{Raw} - V_D}{V_W - V_D}$

Segmentation of vegetation/background¶

$ExG = 2R_{550} - R_{650} - R_{480}$

Otsu (Adaptive) thresholding

Detection of crop rows¶

Seeder coordinates or... Segmented image $\rightarrow$ Image thinning $\rightarrow$ (Morph. operation) $\rightarrow$ Hough transform

FD-SLIC superpixels¶

  • Spectral + spatial info.
  • Number of superpixels: 0.1% - 0.5% of image dimension
  • Texture features...

Relative importance of spectral bands for discriminating superpixels¶

  • Mahalanobis generalized distance $\rightarrow$ Singh (1981) criterion
  • $ D^2_{ij} = (R_i - R_j)^T \Sigma^{-1} (R_i - R_j) $, where $\Sigma$ is a covariance matrix of spectral bands
  • Eigenvectors (SVD) of $\Sigma$

Weed classification¶

  • Machine Learning: CNN x SVM x Mixture discriminant analysis
  • Spx. partitions: Training (70%) x Validation (30%)
  • Apparent error rate
  • Field validation
  • (Deploy the model)

Some references¶

  • Bah, M. D., Hafiane, A., & Canals, R. (2018). Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote sensing, 10(11), 1690.
  • Psalta, A., Karathanassi, V., & Kolokoussis, P. (2016). Modified versions of SLIC algorithm for generating superpixels in hyperspectral images. 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1-5.
  • Li, Y., Al-Sarayreh, M., Irie, K., Hackell, D., Bourdot, G., Reis, M. M., & Ghamkhar, K. (2021). Identification of weeds based on hyperspectral imaging and machine learning. Frontiers in Plant Science, 11, 2324.
  • Singh, D. (1981) The relative importance of characters affecting genetic divergence. Indian Journal Genetics & Plant Breeding, 41:237-245.

Dziękuję bardzo!¶

anderson.silva@ifgoiano.edu.br