Machine Vision Systems in Precision Agriculture for Crop Farming
Efthimia Mavridou,Eleni Vrochidou,George A. Papakostas,Theodore Pachidis,Vassilis G. Kaburlasos +4 more
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TLDR
The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming, to serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture.Abstract:
Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.read more
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A survey of public datasets for computer vision tasks in precision agriculture
Yuzhen Lu,Sierra N. Young +1 more
TL;DR: This paper makes the first comprehensive but not exhaustive review of the public image datasets collected under field conditions for facilitating precision agriculture, which include 15 datasets on weed control, 10 datasets on fruit detection, and 9 datasets on miscellaneous applications.
Journal ArticleDOI
Review of Weed Detection Methods Based on Computer Vision
TL;DR: In this paper, the authors provide an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery.
Journal ArticleDOI
AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture
TL;DR: A deep learning framework AgriSegNet is proposed for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images to increase the efficiency of precision farming techniques.
Journal ArticleDOI
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
TL;DR: An overview of the evolution of GAN architectures followed by a systematic review of their application to agriculture can be found in this article , involving various vision tasks for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects.
Journal ArticleDOI
Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis
TL;DR: This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment.
References
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