Proceedings ArticleDOI
Weed Detection and Classification in High Altitude Aerial Images for Robot-Based Precision Agriculture
Karthik Buddha,Henry J. Nelson,Dimitris Zermas,Nikolaos Papanikolopoulos +3 more
- pp 280-285
TLDR
This work outlines a robotic weed management system and develops the image analysis portion of this system that acts as an integral part of a robotic control loop, providing information on weed location and species to a robotic sprayer that will precisely apply the correct herbicide.Abstract:
With the rise of herbicide-resistant weeds, standard farming practices are no longer effective. Precision agriculture technologies can replace some of the standard practices but require information on the physical distribution of weed and crop species. In this work we outline a robotic weed management system and develop the image analysis portion of this system to test it with real aerial survey data. This portion of the system acts as an integral part of a robotic control loop, providing information on weed location and species to a robotic sprayer that will precisely apply the correct herbicide. The image analysis pipeline we develop proves the feasibility of obtaining this information from high altitude aerial surveys of agricultural land that already commonly take place. Our system performs well enough to reliably detect the presence of weed vegetation and to correctly classify it by species with an accuracy of 93.8 % between four classes of weed in images taken at an effective altitude of 80 feet (24.4 m).read more
Citations
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Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images - eScholarship
TL;DR: A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain.
Posted ContentDOI
GinJinn2: Object detection and segmentation for ecology and evolution
TL;DR: GinJinn2 as discussed by the authors is a toolbox for deep learning-based object detection and instance segmentation on image data, which includes several additional tools for data handling, pre- and postprocessing, and building advanced analysis pipelines.
Proceedings ArticleDOI
Image processing based Smart Weed Removal and Organic Fertilizer Sprinkling Bot – A Systematic Review
TL;DR: The weed and diseased plant control using machine learning algorithms based on image processing is discussed and the machine controls the weeds and the diseased plants around the field autonomously.
Journal ArticleDOI
Neutral weed communities: The intersection between crop productivity, biodiversity, and weed ecosystem services
TL;DR: In this article , the concept of neutral weed communities is proposed, which are weed communities that coexist with crops and do not negatively affect their yield and quality compared with weed-free conditions.
Journal ArticleDOI
A Survey of Weed Identification Using Convolutional Neural Networks
TL;DR: In this paper , a short overview of some significant agricultural research endeavours using convolution neural networks (CNNs) for classification and detection of weeds is presented. But, the authors do not discuss the use of CNNs in the field of agriculture.
References
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