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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Validation of automated measurements of soil tillage variables with laser and ultrasound sensors
TL;DR: In this paper , an electronic profilometer using laser triangulation and ultrasound sensors for measurement of digitally mobilized soil profile was developed, which provides information on roughness, mobilized area, blistering, and thickness in disturbed soils.
Proceedings ArticleDOI
Sustainable Agriculture: An IoT-Based Solution for Early Disease Detection in Greenhouses
TL;DR: In this paper , a convolutional neural network (CNN) model employing deep learning techniques has been developed to detect and diagnose diseases in plants, including bacterial blight, Anthracnose Bacteria, and Thrips insects.
Journal ArticleDOI
AgroAPI platform: An initiative to support digital solutions for agribusiness ecosystems
L. A. S. Romani,Silvio Roberto Medeiros Evangelista,I. Vacari,Daniel Rodrigo de Freitas Apolinário,Glauber José Vaz,E. A. Speranza,D. P. Drucker,S. M. F. S. Massruhá +7 more
TL;DR: The AgroAPI platform as discussed by the authors provides access to data and models for the agricultural sector through Application Programming Interface (API), which is an initiative from the Brazilian Agricultural Research Corporation (Embrapa) and its partners.
Proceedings ArticleDOI
Straight-line Generation Approach using Deep Learning for Mobile Robot Guidance in Lettuce Fields
Chung L. Chang,Hung-Wen Chen +1 more
TL;DR: In this paper , a deep learning-based approach was proposed to recognize various types of objects in images and generate optimal straight-line segments for mobile robots to perform heading corrections in complex environments.
References
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