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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Effect of Computer-assisted Instruction in Agricultural Science: A Focus on Colleges of Education Students in Ghana
TL;DR: In this article , the authors examined how computer assisted instruction (CAI) affected how agricultural science was taught and learned in colleges of education in Ghana and found that pre-service teachers who received CAI performed better than their counterparts who received traditional classroom teaching.
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Feature Extraction of Tea Leaf Images using Dual-Tree Complex Wavelet Transform and Gray Level Co-occurrence Matrix
B H Iswanto,Alma,I Sugihartono +2 more
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Applying textural Law’s masks to images using machine learning
Gulzira Abdikerimova,Moldir Yessenova,A.Ye. Yerzhanova,Z. D. Manbetova,Gulden Y. Murzabekova,Dinara Kaibassova +5 more
TL;DR: In this paper , the use of Laws texture masks in machine learning can help in the analysis of the textural characteristics of objects in the image, which are further identified as pockets of weeds.
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Computer Vision and Machine Learning for Smart Farming and Agriculture Practices
TL;DR: A comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture can be found in this paper , where the authors present a survey of the current state of the art in this field.
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TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
TL;DR: In this paper , a novel diffusion-based model for semantic segmentation of on-plant tomatoes is proposed, which demonstrates state-of-the-art performance, even in challenging environments with highly occluded fruits.
References
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