Computer vision technology in agricultural automation —A review
Reads0
Chats0
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
More filters
Journal ArticleDOI
Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach
TL;DR: In this paper, the authors developed yield prediction models based on a machine-learning approach using satellite-based time-series images and validated them using regression analysis and an artificial neural network (ANN) approach.
Journal ArticleDOI
A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions
TL;DR: In this paper, a modern generic approach has been proposed for the identification and classification of wheat diseases using Decision Trees (DT) and different deep learning models, and results of both algorithms were then verified by domain experts that improved the DT accuracy by 28.5%, CNN accuracy by 4.3%, and resulted in decision rules for wheat diseases in a knowledge-based system.
Journal ArticleDOI
Detection and Characterization of Cherries: A Deep Learning Usability Case Study in Chile
TL;DR: A deep neural-based approach, using a portable artificial vision system to enhance the cherries harvesting estimates, and was able to classify cherries into four sizes, for a better characterization of the production for exportation.
Journal ArticleDOI
A computer vision system for automatic cherry beans detection on coffee trees
Jhonn Pablo Rodríguez,David Camilo Corrales,David Camilo Corrales,Jean-Noël Aubertot,Juan Carlos Corrales +4 more
TL;DR: This approach substitutes the destructive counting method as a first step to estimate coffee production and finds several shortcomings in this methodology as counting errors in the sampling process, insufficient coffee bean samples, significant expenses of costs and time, and coffee beans losses.
Posted ContentDOI
The Hype and Disruptive Technologies of Industry 4.0 in Major Industrial Sectors: A State of the Art
Ocident Bongomin,Aregawi Yemane,Brendah Kembabazi,Clement Malanda,Chikonkolo Mwape M,Sheron Mpofu N,Dan Tigalana +6 more
TL;DR: In this paper, the authors identify industry 4.0 neologisms and illustrate the convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries.
References
More filters
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.
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.