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
Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris
TL;DR: In this article , the authors developed a method of detection of Xanthomonas campestris infection on broccoli leaves based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns.
Book ChapterDOI
Salient Object Detection with Pretrained Deeplab and k-Means: Application to UAV-Captured Building Imagery.
Victor Megir,Giorgos Sfikas,Athanasios Mekras,Christophoros Nikou,Dimosthenis Ioannidis,Dimitrios Tzovaras +5 more
TL;DR: In this paper, a simple technique that can convert a pretrained segmentation neural network to a salient object detector is presented, which is shown to be agnostic to the semantic class of the object of interest and no further training is required.
Journal ArticleDOI
Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation
Muhammad Faisal,Sushovan Chaudhury,K. Sakthidasan Sankaran,S. Raghavendra,R. J. Chitra,M Eswaran,Raja Srinivasa Reddy Boddu +6 more
TL;DR: The region-based Convolutional Neural Network (CNN) is the latest image segmentation and has good detection accuracy based on the Faster R-CNN algorithm and the results obtained are that plastic objects and bottles can be recognized correctly in the picture.
Journal ArticleDOI
Sensors for UAVs dedicated to agriculture: current scenarios and challenges
TL;DR: The overview of the types and application areas of onboard sensors is presented, the trends in the onboard agricultural UAVs’ sensors, their applications and operational characteristics have been presented, and some conclusions and suggestions should allow readers to choose the proper onboard sensors set and the right way of acquiring Uavs for their purposes related to the agricultural area.
Remote sensing in the analysis and characterization of spatial variability of the territory. a study case in Timis County, Romania.
TL;DR: In this paper, the authors used satellite images analysis to evaluate and characterization of land spatial variability in the Livezile-Dolat Protected Area, Timis County, Romania.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
TL;DR: A survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry—wherein the combination of UAV and hyperspectrals plays a center role—is presented in this paper.
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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
TL;DR: This review focuses on the fundamentals of hyperspectral image analysis and its modern applications such as food quality and safety assessment, medical diagnosis and image guided surgery, forensic document examination, defense and homeland security, remote sensing applicationssuch as precision agriculture and water resource management and material identification and mapping of artworks.
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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.