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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Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach
TL;DR: The multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images are investigated and a simple and new method was used as a decision function in the multiple classifiers.
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Systematic Mapping Study on Remote Sensing in Agriculture
José A. García-Berná,Sofia Ouhbi,Brahim Benmouna,Ginés García-Mateos,José Luis Fernández-Alemán,José Miguel Molina-Martínez +5 more
TL;DR: A general recommendation that emerges from this study is to build on existing resources, such as agricultural image datasets, public satellite imagery, and deep learning toolkits, to find the current trends and new opportunities in the area.
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State of the art of urban smart vertical farming automation system: Advanced topologies, issues and recommendations
TL;DR: In this paper, the authors provide a comprehensive review of the concept of USVF using various techniques to enhance productivity as well as its types, topologies, technologies, control systems, social acceptance, and benefits.
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Growing period classification of Gynura bicolor DC using GL-CNN
Xia Hao,Jingdun Jia,Abdul Mateen Khattak,Abdul Mateen Khattak,Li Zhang,Xuchao Guo,Wanlin Gao,Minjuan Wang,Minjuan Wang +8 more
TL;DR: The proposed model merges the features using a network fusion strategy to expand the feature representation on the basis of the intact leaf and leaf patch image sets and reaches 95.63%, which is the best in the classification task.
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Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics
TL;DR: A simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program, and provides a new idea for related insect behavior identification research.
References
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Machine Learning in Agriculture: A Review.
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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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
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Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
TL;DR: This work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley.
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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.