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Advances in Measurement, Instrument, and Sensing Methods for Sustainable Agriculture

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3780

Special Issue Editors

1. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 310012, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310012, China
Interests: biosensing; rapid detection of pathogenic microorganisms; early diagnosis of diseases
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Guest Editor
School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
Interests: UAV-based remote sensing; hyperspectral imaging; deep learning; precision agriculture; information fusion; SIF; vegetation indices
State Key Laboratory of Rice Biology, China Rice Research Institute, Hangzhou 310018, China
Interests: basic biological characteristics of weeds in rice fields; weed resistance to herbicides; development of mechanisms of resistance

Special Issue Information

Dear Colleagues,

Sustainable agriculture is agriculture that continuously meets contemporary human needs with regard to the quantity and quality of agricultural products through the management, conservation, and sustainable use of natural resources and through the adaptation of farming systems and technologies without harming the interests of future generations. Additionally, sustainable agriculture maintains and makes rational use of land, water, plant, and animal resources without causing environmental degradation, while being technologically appropriate and feasible, economically viable, and widely accepted by society. Sustainable agriculture is about to shift the paradigm of our current high-input, high-pollution, high-waste agriculture production. To achieve this, modern agriculture needs to treat agricultural information as a factor of agricultural production, and use modern information technology for the visual expression, digital design, and information management of agricultural objects, the environment, and the whole process. Agriculture needs modern technology, especially new measurements, instruments, and sensor methods, more than ever to make it smart. We have seen advances in this field, such as hyperspectral cameras, multispectral cameras, thermal sensors, LiDAR, SAR, etc., mounted on different platforms such as UAVs, mobile vehicles, and towers, and implemented with different analysis methods including graphic methods, computer vision, deep learning, etc. to quantify factors that influence field production. We have seen innovations in measurements and instruments as well, such as handheld devices, ground-penetrating instruments, electronic noses/tongues, biosensors, etc. We have seen the breakthrough in sensing technology that introduced new approaches to agriculture, such as wireless sensor networks, cloud/edge computing, wide-area networks, Internet of Things (IoT), precise satellite navigation/localization, air quality sensors, and soil sensors. To embrace the latest science and technology advances in this field, we are pleased to announce this Special Issue, “Advances in Measurements, Instruments, and sensing methods for Sustainable Agriculture”, which will include all possible topics related to this research field.

For this Special Issue, authors are invited to publish articles focusing on their recent scientific progress and innovations in measurements, instruments, and sensing methods for sustainable agriculture. We welcome novel research, reviews, and opinion papers covering all related topics that promote the application of the latest measurement and sensing techniques, including thermo-acoustic-optical-electromagnetic-based sensors, as well as the latest instruments used in agriculture, such as those used in plant or animal agricultural production, including  for agricultural soils, water, pests, controlled environments, structures, and wastes, in addition to those used on the plants and animals themselves. On-farm, post-harvest operations that considered to be a part of the agricultural process (such as drying, storage, logistics, production assessment, trimming, and separation of plant and animal material) will also be covered.

Dr. Lizhou Xu
Dr. Yanchao Zhang
Dr. Wei Tang
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. Sustainability 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 2400 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

  • agricultural instrument
  • remote sensing
  • plant phenomics
  • biosensors
  • electronic nose/tongue
  • electrochemical sensors
  • optical sensors
  • sustainable agriculture

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Published Papers (2 papers)

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Research

18 pages, 507 KiB  
Article
Substantiation of the Risk Neutralization Mechanism in the Financial Security Management of Agricultural Enterprises
by Nadiia Davydenko, Natalia Wasilewska, Zoya Titenko and Mirosław Wasilewski
Sustainability 2024, 16(3), 1159; https://doi.org/10.3390/su16031159 - 30 Jan 2024
Viewed by 1327
Abstract
In the context of the Ukrainian economy reforming and ensuring that economic activity is conducted in accordance with current global economic trends, special attention should be paid to solving the problem of neutralizing risks in the financial security management of agricultural enterprises. The [...] Read more.
In the context of the Ukrainian economy reforming and ensuring that economic activity is conducted in accordance with current global economic trends, special attention should be paid to solving the problem of neutralizing risks in the financial security management of agricultural enterprises. The purpose of this article is to substantiate the risk neutralization mechanism in the management of financial security for enterprises in the agrarian sector. In writing this article, we used scientific methods such as modeling (to determine the impact of a certain set of factors on the level of enterprises’ financial security), analysis and synthesis (to find out the reasons that cause changes in the studied indicators), tabular and graphical (to present the study results), and abstract and logical (to make theoretical and methodological generalizations). The study results presented in this paper are important for developing offers for neutralizing risks in the financial security management of agricultural enterprises. Full article
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19 pages, 8148 KiB  
Article
Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods
by Yiyang Shen, Ziyi Yan, Yongjie Yang, Wei Tang, Jinqiu Sun and Yanchao Zhang
Sustainability 2024, 16(2), 632; https://doi.org/10.3390/su16020632 - 11 Jan 2024
Cited by 6 | Viewed by 1706
Abstract
Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. [...] Read more.
Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research comprised three parts: hyperspectral data preprocessing, spectral feature extraction, and model construction. To begin, the preprocessing of hyperspectral data involved geometric distortion correction, relative radiometric calibration, and rice canopy mask construction. Challenges in geometric distortion correction were addressed by tracking linear features during flight and applying a single-line correction method. Additionally, the NIR reflectance threshold method was applied for rice canopy mask construction, which was subsequently utilized for average reflectance extraction. Then, spectral feature extraction was carried out to reduce multicollinearity in the hyperspectral data. Recursive feature elimination (RFE) was then employed to identify the optimal feature set for model performance. Finally, six machine learning regression models (SVR, RFR, AdaBoost, XGBoost, Ridge, and PLSR) were used for rice yield estimation, achieving significant results. PLSR showed the best R2 of 0.827 with selected features, while XGBoost had the best R2 of 0.827 with full features. In addition, the spatial distribution of absolute error in rice yield estimation was assessed. The results suggested that this UAV-borne imaging hyperspectral-based approach held great potential for crop yield estimation, not only for rice but also for other crops. Full article
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