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Sensors
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Advances in Remote Sensing and IoT Technologies in Smart Farming
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Advances in Remote Sensing and IoT Technologies in Smart Farming

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 2663

Special Issue Editors

Prof. Dr. Ran Meng

E-Mail Website
Guest Editor
College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
Interests: plant stress; multiscale remote sensing; vegetation dynamics; smart agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Zhao

E-Mail Website
Guest Editor
International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen 518055, China
Interests: artificial intelligence; IoT; wireless sensing; signal processing; embedded networked systems
Dr. Lin Yuan

E-Mail Website
Guest Editor
School of Information Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Interests: plant diseases and pests; remote sensing; hyperspectral analysis; smart agriculture; monitoring model
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Peng

E-Mail Website
Guest Editor
Institute for Sustainability, Energy, and Environment (ISEE), University of Illinois Urbana-Champaign, Urbana, IL, USA
Interests: remote sensing; crop modeling; precision agriculture; agroecology; ecohydrology

Special Issue Information

Dear Colleagues,

Smart farming refers to using advanced technologies to inform farming management decisions, which can increase the quantity and quality of agricultural products (grain, livestock, and dairy) while optimizing resource (e.g., water, fertilizer, and pesticide) use. Thanks to the tremendous progress of modern technologies, such as remote sensing and the Internet of Things (IoT), smart farming is becoming widespread for ensuring sustainable development in agriculture. The applications of smart farming technologies range from using IoT sensors measuring soil, plant, and environmental conditions to in-time monitoring crop stress, early prediction of crop yield, and using advanced data analytics to support site-specific agricultural management. We are pleased to announce a Special Issue entitled “Advances of Remote Sensing and IoT Technologies in Smart Farming”. This issue aims to present state-of-the-art research on the use of remote sensing and IoT techniques for crop growth monitoring, soil moisture estimation, crop stress detection, crop yield prediction, plant phenotyping, and any other related novel applications in smart farming.

Prof. Dr. Ran Meng
Dr. Yang Zhao
Dr. Lin Yuan
Dr. Bin Peng
Guest Editors

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 submissions that pass pre-check are 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. Sensors 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 2600 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

  • multi-source remote sensing
  • smart farming
  • IoT
  • crop production
  • sustainable development

Published Papers (1 paper)

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Research

16 pages, 4484 KiB  
Article
Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network
by Li Ma, Lei Zhao, Liying Cao, Dongming Li, Guifen Chen and Ye Han
Sensors 2022, 22(20), 7777; https://doi.org/10.3390/s22207777 - 13 Oct 2022
Cited by 3 | Viewed by 2106
Abstract
Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. [...] Read more.
Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong’an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing. The LeNet-5 convolutional neural network model was chosen as the deep learning model, which was improved based on the basic model. The evaluation metrics were selected as the root mean square error (RMSE) and the coefficient of determination R2. Through comparison, the R2 of the improved model was found to be higher than that of the linear regression method, Support Vector Machines (SVM) (RMSE = 2.471, R2 = 0.4035), and Random Forest (RF) (RMSE = 2.577, R2 = 0.4913). The result shows that: (1) It is feasible to use the multispectral data extracted from remote sensing images for soil organic matter content inversion based on the deep learning model with a minimum RMSE of 2.979 and with the R2 reaching 0.89. (2) The choice of features has an impact on the prediction of the model to a certain extent. After ranking the importance of features, selecting the appropriate number of features for inversion provides better results than full feature inversion, and the computational speed is improved. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and IoT Technologies in Smart Farming)
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