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Automatic Learning in Agriculture: A Survey
2021
International Journal of Computing and Digital Systems
Preserved Fulltext
This paper presents a review of existing applications of machine learning in agriculture with a focus on the applications of Deep Reinforcement Learning techniques in agriculture. ...
While, in reinforcement learning, sequential decision making happens and the next input depends on the decision of the machine. ...
This research used a Gaussian Mixture Model to extract moving objects. Then a Region-based Convolutional Neural Network (R-CNN) was used for the classification task. ...
doi:10.12785/ijcds/1001118
fatcat:sq75r6nnmvccpd6d6cfge4kzpq
A Survey of Deep Learning Techniques for Weed Detection from Images
[article]
2021
arXiv
pre-print
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Also, a crop in one setting can be considered a weed in another. ...
Weed detection in crops from imagery is inherently a challenging problem because both weeds and crops have similar colours ('green-on-green'), and their shapes and texture can be very similar at the growth ...
Two of the frequently used architectures are Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) (Hosseini et al., 2020; LeCun et al., 2015) . ...
arXiv:2103.01415v1
fatcat:jnqee4f33fasvgthz2ax6nfxay
Proceedings Of The 7Th Asian-Australasian Conference On Precision Agriculture
[article]
2017
Zenodo
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Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture, ABSTRACTS ...
Poster
Machine vision based system for flower counting in strawberry plants Strawberry flowers are white in color with a yellow pollen at the center, which later becomes a fruit. ...
Strawberry yield can be estimated by counting the number of flowers in a field in advance of harvesting. ...
doi:10.5281/zenodo.1006670
fatcat:wupvtcswufg3bkpm3mdkrn5a4m
Proceedings Of The 7Th Asian-Australasian Conference On Precision Agriculture
[article]
2017
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture, ABSTRACTS ...
Strawberry yield can be estimated by counting the number of flowers in a field in advance of harvesting. ...
Poster
Machine vision based system for flower counting in strawberry plants Methods: Strawberry flowers could be at different stages of maturation during imaging. ...
doi:10.5281/zenodo.1006669
fatcat:jb6sqa7ayngmlg7amwlpdkfmcy
Machine Learning in Agriculture: A Comprehensive Updated Review
2021
Sensors
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A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. ...
to a more systematic research on this topic. ...
A usual kind of DNNs are the Convolutional Neural Networks (CNNs), whose layers, unlike common neural networks, can set up neurons in three dimensions [87] . ...
doi:10.3390/s21113758
pmid:34071553
fatcat:moehdvs6efdpxpklidutmw2ary
Recent developments of the Internet of Things in Agriculture: A Survey
2020
IEEE Access
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With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. ...
A rise in the population has immensely increased the pressure on the agriculture sector. ...
Network
CNN
Convolutional Neural Network
DSS
Decision Support System
FAO
Food and Agricultural Organization of the United
Nations
FCA
Front forward Communication Area
GA
Genetic Algorithms ...
doi:10.1109/access.2020.3009298
fatcat:b7tt3mizaffbrgpaytazftaine
Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests
2021
Inventions
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The applicability of deep convolutional neural networks (DCNN) with focus on AlexNet and GoogLeNet were evaluated for the classification problem. ...
A deep convolutional neural network (DCNN)-based approach for automated yellow rust disease (caused by Puccinia striiformis f. sp. ...
doi:10.3390/inventions6020029
doaj:bad904d96b2b4bc1852ecd689b5fe63d
fatcat:k7qcrjbsfnb35kr3jpvnqckpai
MICRO AND MACRO VIEWS OF THE MAIZE-SETOSPHAERIA TURCICA PATHOSYSTEM
2020
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These data were used to train a convolutional neural network (CNN) to high accuracy, and a fully-connected conditional random field (CRF) was used to segment images into lesion and non-lesion areas using ...
I used RNA-seq to explore the transcriptomic aspects of infection, with a focus on the pathogen's transition from biotrophy to necrotrophy and the impacts of pathogen virulence/avirulence in the presence ...
We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy ...
doi:10.7298/5yt0-pd05
fatcat:bi5h4xl7dnfbbgy5ehrlvgfpey