Special Issue "Machine Learning in Agricultural Informatization"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 20 January 2022.

Special Issue Editor

Dr. Minjuan Wang
E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: image data processing; agricultural information technology

Special Issue Information

Dear Colleagues,

Agricultural machine learning is not a mysterious trick or magic, but a set of well-defined models that collect specific data and apply specific algorithms to achieve expected results. Accurate data sensing and processing are basic part of quantitative decision-making in smart agriculture management. Image sensing provides multi-dimensional information for agriculture detection, such as color, visible-near infrared spectroscopy, thermal radiation and 3D representation. The traditional way of analyzing these datasets focuses on the characteristics of color, morphology, texture, spectral reflection, etc. The limitations of sample mounts and extracted features always lead to problems such as insufficient noise reduction and the low accuracy of the recognition and detection models, especially for complex background changes and unknown samples. Deep learning (DL), a subset of machine learning approaches, emerged, and combined neural networks to extract and represent the high-level features of image. This could help to build reliable predictions of complex and uncertain phenomena in agriculture.

This Special Issue aims to explore the state of the art of the latest advances in the estimation of machine learning in the agricultural field. This will also cover studies that adapt existing algorithms to agriculture information, as well as literature reviews.

Dr. Minjuan Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • convolutional neural network
  • agricultural dataset
  • big data
  • agriculture detection

Published Papers (1 paper)

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Research

Article
Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)
Appl. Sci. 2021, 11(14), 6657; https://doi.org/10.3390/app11146657 - 20 Jul 2021
Viewed by 506
Abstract
Pollen analysis and the classification of several pollen species is an important task in melissopalynology. The development of machine learning or deep learning based classification models depends on available datasets of pollen grains from various plant species from around the globe. In this [...] Read more.
Pollen analysis and the classification of several pollen species is an important task in melissopalynology. The development of machine learning or deep learning based classification models depends on available datasets of pollen grains from various plant species from around the globe. In this paper, Cretan Pollen Dataset v1 (CPD-1) is presented, which is a novel dataset of grains from 20 pollen species from plants gathered in Crete, Greece. The pollen grains were prepared and stained with fuchsin, in order to be captured by a camera attached to a microscope under a ×400 magnification. In addition, a pollen grain segmentation method is presented, which segments and crops each unique pollen grain and achieved an overall detection accuracy of 92%. The final dataset comprises 4034 segmented pollen grains of 20 different pollen species, as well as the raw data and ground truth, as annotated by an expert. The developed dataset is publicly accessible, which we hope will accelerate research in melissopalynology. Full article
(This article belongs to the Special Issue Machine Learning in Agricultural Informatization)
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