Automated Weed Detection in Aerial Imagery with Context
Delia Bullock, Andrew Mangeni, Tyr Wiesner-Hanks, Chad DeChant, Ethan, L. Stewart, Nicholas Kaczmar, Judith M. Kolkman, Rebecca J. Nelson, Michael, A. Gore, and Hod Lipson

TL;DR
This paper presents a context-aware convolutional neural network approach for improved weed detection in aerial imagery, significantly reducing classification errors by incorporating surrounding environmental information.
Contribution
The study introduces a novel context-based classification method that enhances weed detection accuracy in aerial images by using surrounding image information during training and testing.
Findings
Context nearly halved error rate from 7.1% to 4.3%.
Network with context outperforms without after only one epoch.
Context technique is especially beneficial in agricultural imagery where similar plant parts appear.
Abstract
In this paper, we demonstrate the ability to discriminate between cultivated maize plant and grass or grass-like weed image segments using the context surrounding the image segments. While convolutional neural networks have brought state of the art accuracies within object detection, errors arise when objects in different classes share similar features. This scenario often occurs when objects in images are viewed at too small of a scale to discern distinct differences in features, causing images to be incorrectly classified or localized. To solve this problem, we will explore using context when classifying image segments. This technique involves feeding a convolutional neural network a central square image along with a border of its direct surroundings at train and test times. This means that although images are labelled at a smaller scale to preserve accurate localization, the network…
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Taxonomy
TopicsSmart Agriculture and AI · Remote Sensing in Agriculture · Remote-Sensing Image Classification
MethodsTest
