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
Peanut Defect Identification Based on Multispectral Image and Deep Learning
TL;DR: In this paper , a multi-target identification method based on the multispectral system and improved Faster RCNN is proposed to achieve the non-destructive detection of peanut defects, a texture-based attention and a feature enhancement module were designed to enhance the performance of its backbone.
Book ChapterDOI
Various Type of Crops and Trees Detection Using Clustering Technique Through Image Processing
TL;DR: Based on ResNet-18 and MobileNetV1, this article created a custom identification neural network to detect chili, eggplant, and potato for the NVIDIA® Jetson Nano Developer Kit.
Proceedings ArticleDOI
REAM: Revolutionary Environmental and Agricultural Monitoring System
TL;DR: In this article, the authors used the Internet of Things (IoT) technologies and computer vision to tackle the agriculture sector problems by using an actual farm site and real-time crops monitoring solution as a test bed.
Journal ArticleDOI
Modern Craft Product Design Using Digital Technology Combined with Visual Sensing System in Complex Environment
Zhifeng Zhao,Du Chen +1 more
TL;DR: In this article , an improved oriented fast and rotated brief (ORB) feature matching algorithm is designed for target image feature extraction, which improves the accuracy of feature extraction and has important significance in image recognition.
References
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