Corn-Plant Counting Using Scare-Aware Feature and Channel Interdependence

Yong Yang Ma, Zhan Li Sun, Zhigang Zeng, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Corn-plant counting is an important process for predicting corn yield and analyzing corn-plant phenotypes. In this letter, an effective corn-plant counting method is proposed, which is based on utilizing the scale-aware (SA) contextual feature and channel interdependence (CI). Given the Visual Geometry Group (VGG) Network features, the SA features are extracted by spatial pyramid pooling to derive multiscale context information. In order to utilize the channel interdependent information, the VGG features are integrated via a channel attention module. Moreover, an encoder-decoder structure is constructed to fuse the SA features and the CI-based features. Considering the sparsity of a corn plant, a hybrid loss function is adopted to train the network, by considering a density map loss function and an absolute count loss function. Experimental results demonstrate the effectiveness of the proposed method for corn-plant counting.

Original languageEnglish
JournalIEEE Geoscience and Remote Sensing Letters
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Channel attention module
  • Computer architecture
  • corn-plant counting
  • Feature extraction
  • Lighting
  • scale-aware (SA) feature
  • Sun
  • Testing
  • Training
  • Visual Geometry Group (VGG) feature.
  • Visualization

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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