Computer vision technology in agricultural automation —A review
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TLDR
It is found that the existing technology can help the development of agricultural automation for small field farming to achieve the advantages of low cost, high efficiency and high precision, but there are still major challenges.About:
This article is published in Information Processing in Agriculture.The article was published on 2020-03-01 and is currently open access. It has received 228 citations till now.read more
Citations
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Journal ArticleDOI
A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images
TL;DR: In this article , a novel CNN architecture was devised to classify weed images captured by sprayer drones using the Harris Hawk Optimization algorithm (HHO) by selecting the most appropriate parameters.
Journal ArticleDOI
Pest Localization Using YOLOv5 and Classification Based on Quantum Convolutional Network
Javeria Amin,Muhammad Almas Anjum,Rida Zahra,Muhammad Imran Sharif,Seifedine Kadry,Lukas Sevcik +5 more
TL;DR: YOLOv5 as mentioned in this paper is trained using the optimal learning hyperparameters which more accurately localize the pest region in plant images with 0.93 F1 scores, and after localization, pest images are classified into Paddy with pest/Paddy without pest using the proposed quantum machine learning model, which consists of fifteen layers with two-qubit nodes.
Proceedings ArticleDOI
Computational Tool for Analysis of Strains Based on Optical Flow Approach
Andriy Dashkevich,Oleksii Vodka +1 more
TL;DR: An approach to solve the problem of the finding of displacement vectors in the two consecutive video frames of the strain test process video is presented and improvements to feature extraction method of establishing of robust point correspondences between pair of key points in video frames are proposed.
References
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TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
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Modern Trends in Hyperspectral Image Analysis: A Review
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Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.
Guan Wang,Yu Sun,Jianxin Wang +2 more
TL;DR: The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set.