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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Proceedings ArticleDOI
Classification for Crop Pest on U-SegNet
TL;DR: In this article , the authors used pre-trained deep learning architectures like Unet and ResNet to compare the proposed model to the suggested U-SegNet for insect categorization.
Journal ArticleDOI
Modern approaches to managing change in agricultural enterprises
TL;DR: In this paper, the authors developed a model for managing changes in agriculture, based on the use of agricultural engineering tools. Theoretical studies were carried out using the methods of system analysis and generalization of existing scientific developments in the development of agriculture digitalization.
Book ChapterDOI
A Deep Learning Approach for Lantana Camara Weed Detection and Localization in the Natural Environment
Posted Content
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
TL;DR: In this article, the design of such systems from a hybrid intelligence (HI) perspective is considered and four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) are identified to inform their derived meta-requirements and design principles.
Journal ArticleDOI
Simulation-Aided Development of a CNN-Based Vision Module for Plant Detection: Effect of Travel Velocity, Inferencing Speed, and Camera Configurations
TL;DR: In this article , the authors proposed the overlapping rate (ro), which is the ratio of the camera field of view (S) and inferencing speed (fps) to the travel velocity (v⇀) to theoretically predict the plant detection rate (rd) of an MVS and aid in developing a CNN-based vision module.
References
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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
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