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
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Posted ContentDOI
Segmentation of Weeds and Crops Using Multispectral Imaging and Crf-Enhanced U-Net
Proceedings ArticleDOI
Comparison of Image Extraction Model for Cocoa Disease Fruits Attack in Support Vector Machine Classification
TL;DR: In this article , the authors compared the results of four feature extraction models in the case of early recognition of disease attacks on cocoa fruits, including Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Hue Saturation Value (HSV), and GLCH.
Journal ArticleDOI
An efficient approach for automated system to identify the rice crop disease using intensity level based multi-fractal dimension and twin support vector machine
Shashank Chaudhary,Upendra Kumar +1 more
TL;DR: In this paper , three different types of classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Twin Support Vector Machines (TWSVM) were used.
A decoupled search deep network framework for high-resolution remote sensing image classification
Kun Wang,Ling Han,Liangzhi Li +2 more
TL;DR: In this article, a decoupled search approach was designed to optimize this three-layer search space, which enables the development of autonomous designs of network skeletons, namely for HRI feature extraction and classification.
Journal ArticleDOI
An AIoT Framework for Precision Agriculture
TL;DR: In this paper , the authors present an AIoT framework for modern agriculture by implementing data-driven solutions based on low-cost devices and open source technologies, empowered by edge intelligence, which will help not only in increasing quantity and quality of food production, but also in enhancing the efficiency of agricultural operations.
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
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Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.