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Application of Computer Vision Technology in Agricultural Field

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TLDR
The development of computer vision technology mainly in detection and grading of the quality of agricultural products, crop monitoring, automation of agricultural production, crop disease identification and other aspects is reviewed and the prospect for future development is discussed.
Abstract
Computer vision technology has been widely applied to various fields of agricultural development, and with the rapid development of computer technology, graphics and image processing technology, enormous progress has been achieved on its applications in agriculture. This paper has reviewed and summarized the development of computer vision technology mainly in detection and grading of the quality of agricultural products, crop monitoring, automation of agricultural production, crop disease identification and other aspects and has discussed the prospect for future development.

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Journal ArticleDOI

Machine Vision based Fruit Classification and Grading - A Review

TL;DR: A detailed overview of the process of fruit classification and grading has been presented and some extraction methods like Speeded Up Robust Features (SURF), Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are discussed with the common features of fruits like color, size, shape and texture.
Journal ArticleDOI

Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines

TL;DR: A classification method for images of grapevine buds detection in natural field conditions using well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier.
Journal ArticleDOI

A contextualized approach for segmentation of foliage in different crop species

TL;DR: This work investigates the role of vegetative indexes and color spaces in different formulations of machine learning algorithms, and proposes a new formulation that consists of combining the CIE Luv color space and support vector machines in order to benefit from contextualized information obtained through neighboring pixels.
Journal ArticleDOI

Identification and Counting of Soybean Aphids from Digital Images Using Shape Classification

TL;DR: In this paper, an automatic image processing method was developed to identify and count aphids as well as exoskeletons and leaf spots on soybean leaves based on shape analysis.
Proceedings ArticleDOI

An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM

TL;DR: The experimental results show that the proposed image processing method can obtain green apple segmentation and lesion detection with good efficiency and robustness in complex natural orchard environment.
References
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Journal Article

Rice Appearance Quality Detection Based on Computer Vision

TL;DR: An approach, which detects rice appearance quality parameters such as yellow grains,grain shape automatically based on computer vision technology instead of visual observation, is presented and it is shown that the approach is effective and reliable.
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