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Automatic Learning in Agriculture: A Survey

Alia AlKameli, Mustafa Hammad
2021 International Journal of Computing and Digital Systems  
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]

A S M Mahmudul Hasan, Ferdous Sohel, Dean Diepeveen, Hamid Laga, Michael G.K. Jones
2021 arXiv   pre-print
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]

W Nelson
2017 Zenodo  
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]

W Nelson
2017 Zenodo  
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

Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis
2021 Sensors  
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

Vippon Preet Kour, Sakshi Arora
2020 IEEE Access  
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

Bhuwan Kashyap, Ratnesh Kumar
2021 Inventions  
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

Tyr Wiesner-Hanks
2020
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