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Sensors and Data-Driven Precision Agriculture—Second Edition
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Sensors and Data-Driven Precision Agriculture—Second Edition

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

Deadline for manuscript submissions: 10 December 2025 | Viewed by 287

Special Issue Editors

Prof. Dr. Takaharu Kameoka

E-Mail Website
Guest Editor
Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Interests: agricultural; food and bioinformation engineering; optical foodomics
Special Issues, Collections and Topics in MDPI journals
Dr. Toshihiro Fukatsu

E-Mail Website
Guest Editor
Institute of Agricultural Machinery, National Agriculture and Food Research Organization, Tsukuba 3050856, Japan
Interests: field monitoring; agent system; sensor network
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kazuki Kobayashi

E-Mail Website
Guest Editor
Faculty of Engineering, Shinshu University, 4-17-1, Wakasato, Nagano 380-8553, Japan
Interests: field monitoring and artificial intelligence based phenotyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Sensors and Data-Driven Precision Agriculture”, we are pleased to announce the next in the series, entitled “Sensors and Data-Driven Precision Agriculture—Second Edition”.

Now that agricultural machinery has become computerized and data-integrated systems are being realized, the data-driven nature of precision agriculture, which was originally built as part of a mechanized system, has become clear. Measuring crop vigor, which used to be extremely difficult, is gradually becoming possible with the evolution of multiband optical phenotyping, drones, and robots.

In light of the current state of agriculture, this Special Issue attempts to provide a glimpse into the forefront of science-based data-driven agriculture, for example, the development of multimodal sensors for measuring the soil environment, including soil microorganisms; MEMS multispectroscopic devices for measuring the light environment that contributes to photosynthesis and photomorphogenesis; or scenarios for measuring and controlling both the environment and crops in institutional cultivation, where environmental control is possible. Other open-field research topics include measurement of the growing environment; phenotyping and control strategies using 2D/3D image sensing and machine learning; and measurement of the communication between soil microorganisms and crops using advanced sensors.

Prof. Dr. Takaharu Kameoka
Dr. Toshihiro Fukatsu
Prof. Dr. Kazuki Kobayashi
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

  • multimodal sensors
  • MEMS multispectroscopic devices
  • phenotyping
  • drones
  • robot
  • photosynthesis
  • precision agriculture

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Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 7086 KiB  
Article
Honeybee Colony Growth Period Recognition Based on Multivariate Temperature Feature Extraction and Machine Learning
by Chuanqi Lu, Lin Li, Denghua Li, Qiuying Huang and Wei Hong
Sensors 2025, 25(13), 3916; https://doi.org/10.3390/s25133916 - 23 Jun 2025
Viewed by 170
Abstract
Identifying the growth period of bee colonies can guide beekeepers to make better decisions and promote the development of bee colonies. Unlike traditional manual experience-based recognition, this paper proposes a new approach, which combines multivariate temperature feature extraction and machine learning to intelligently [...] Read more.
Identifying the growth period of bee colonies can guide beekeepers to make better decisions and promote the development of bee colonies. Unlike traditional manual experience-based recognition, this paper proposes a new approach, which combines multivariate temperature feature extraction and machine learning to intelligently recognize the growth period of bee colonies. Firstly, the year-round temperature data from 38 hives in Tai’an and Guilin was collected. Then, the 17 time domain characteristic indices were extracted from this dataset. To acquire the most sensitive features, the impact of different time scales on temperature feature extraction was analyzed. Subsequently, principal component analysis (PCA) was employed to reduce the dimensionality of the original feature vectors, thereby decreasing computational load and enhancing feature sensitivity. Finally, six machine learning algorithms, including both supervised and unsupervised learning, were utilized to identify the growth period of bee colonies. The results demonstrate that the proposed features can effectively characterize the growth period of bee colonies, and the BP method performs best in predicting growth period categories, with an MAE of only 1.45%. Moreover, the identification results of different regions also prove the practicability of the proposed method. Full article
(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture—Second Edition)
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