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New Technological Applications in Agriculture for the Development of the Circular Bioeconomy

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

Deadline for manuscript submissions: 2 October 2024 | Viewed by 4256

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Special Issue Editors

Prof. Dr. Yingkuan Wang

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Guest Editor

Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
Interests: agricultural mechanization; automatization and informatization

Prof. Dr. Liyong Fu

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Guest Editor

Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: forest growth and yield modeling; forest biomass estimation
Special Issues, Collections and Topics in MDPI journals

Dr. Jianbo Shen

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Guest Editor

National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Interests: agricultural informatization

Special Issue Information

Dear Colleagues,

With the improvement in people's living standard and the strengthening of environmental protection awareness, environmentally friendly green food and organic food are being paid increasing amounts of attention, and so the agricultural circular economy is put forward, aiming to achieve a balance between agricultural production and environmental protection. Increasing amounts of technologies are emerging to be widely used in agriculture. For example, green prevention and control technology can prevent and control pests and diseases and reduce pesticide residues in soil and agricultural products. Soil testing and formulated fertilization is used for quantitative fertilization in farmland to improve the use efficiency of fertilizer, avoid fertilizer waste, and ensure the rationality of fertilization. Deep learning methods can automatically identify and classify crops using image recognition technology, achieve the automation and intelligence of the agricultural production process, and improve agricultural production efficiency. In addition, new concepts such as "Internet plus agriculture" and "digital villages" are proposed.

Developing an agricultural circular economy is an inevitable choice to protect the rural ecological environment and for sustainable development in agriculture. For this process, technological innovation is particularly important. On the premise of respecting and utilizing the laws of nature, cross integration with new technologies is an important development direction of the agricultural circular economy in the future, which will inject a new impetus into increasing farmers' incomes and rural economic and social development.

This Special Issue aims to provide a platform to publicize the agricultural circular economy and report the important research progress of new technologies, new methods, and new equipment in the circular agricultural economy, focusing on new applications of agricultural informatization, intelligent equipment technology, and green prevention and control technology. We welcome submissions from a variety of research directions, including, but not limited to, the following topics:

(1) Research progress of green prevention and control technology in agricultural circular economy;

(2) Research progress of Internet of Things technology in agricultural circular economy;

(3) Research progress of agricultural information technology in agricultural circular economy;

(4) Research progress of intelligent equipment technology in agricultural circular economy;

(5) Research progress of soil testing and formulated fertilization in agricultural circular economy;

(6) Research progress of artificial intelligence technology in agricultural circular economy;

(7) Research progress of "5S" technology in agricultural circular economy;

(8) Research progress of big data technology in agricultural circular economy.

We look forward to receiving your contributions.

Prof. Dr. Yingkuan Wang
Prof. Dr. Liyong Fu
Dr. Jianbo Shen
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 circular economy
digital villages
new technologies
new methods
new equipment

Published Papers (4 papers)

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Research

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21 pages, 1351 KiB

Open AccessArticle

Exploring the Intelligent Emergency Management Mode of Rural Natural Disasters in the Era of Digital Technology

by Jimei Yang, Hanping Hou and Hanqing Hu

Sustainability 2024, 16(6), 2366; https://doi.org/10.3390/su16062366 - 13 Mar 2024

Viewed by 726

Abstract

In recent years, rural areas of China have experienced frequent occurrences of various natural disasters. These calamities pose significant threats to the safety, property, and mental well-being of rural residents while also presenting substantial obstacles to the sustainable development of the rural economy. Currently, emergency management in China faces several challenges such as inadequate emergency institutions, insufficient security policies, weak disaster infrastructure, and difficulties in information sharing. In light of this situation, we propose an intelligent command mode based on modern digital technology that capitalizes on its advantages and integrates early warning systems with decision-making processes and rescue operations to establish a comprehensive emergency event processing system. This innovative approach opens up new avenues for exploring and researching effective modes of rural emergency management. The article elaborates on how the construction of a smart rural emergency management mode facilitates the digital integration of disaster elements while enhancing the efficiency of emergency response efforts and promoting sustainable development. The research methodology employed includes literature review methods along with field research techniques and analysis methods. Finally, this discussion evaluates both the benefits and challenges associated with implementing this mode within rural emergency management practices. Full article

(This article belongs to the Special Issue New Technological Applications in Agriculture for the Development of the Circular Bioeconomy)

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24 pages, 7451 KiB

Open AccessArticle

Multi-Target Rumination Behavior Analysis Method of Cows Based on Target Detection and Optical Flow Algorithm

by Ronghua Gao, Qihang Liu, Qifeng Li, Jiangtao Ji, Qiang Bai, Kaixuan Zhao and Liuyiyi Yang

Sustainability 2023, 15(18), 14015; https://doi.org/10.3390/su151814015 - 21 Sep 2023

Viewed by 1005

Abstract

Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model’s performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the cow head region and the mouth region. The ResNet-50-FPN network is employed to extract the cow mouth features, and the CBAM attention mechanism is incorporated to further improve the algorithm’s detection accuracy. Subsequently, the object detection results are combined with optical flow information to eliminate false detections. Finally, an interpolation approach is adopted to design a frame complementary algorithm that corrects the detection frame of the cow mouth region. This interpolation algorithm is employed to rectify the detection frame of the cow’s mouth region, addressing the issue of missed detections and enhancing the accuracy of ruminant mouth region detection. To overcome the challenges associated with the inaccurate extraction of small-scale optical flow information and interference between different optical flow information in multi-objective scenes, an enhanced GMFlowNet-based method for multi-objective cow ruminant optical flow analysis is proposed. To mitigate interference from other head movements, the MeanShift clustering method is utilized to compute the velocity magnitude values of each pixel in the vertical direction within the intercepted ruminant mouth region. Furthermore, the mean square difference is calculated, incorporating the concept of range interquartile, to eliminate outliers in the optical flow curve. Finally, a final filter is applied to fit the optical flow curve of the multi-object cow mouth movement, and it is able to identify rumination behavior and calculate chewing times. The efficacy, robustness, and accuracy of the proposed method are evaluated through experiments, with nine videos capturing multi-object cow chewing behavior in different settings. The experimental findings demonstrate that the enhanced Faster R-CNN algorithm achieved an 84.70% accuracy in detecting the ruminant mouth region, representing an improvement of 11.80 percentage points over the results obtained using the Faster R-CNN detection approach. Additionally, the enhanced GMFlowNet algorithm accurately identifies the ruminant behavior of all multi-objective cows, with a 97.30% accuracy in calculating the number of ruminant chewing instances, surpassing the accuracy of the FlowNet2.0 algorithm by 3.97 percentage points. This study provides technical support for intelligent monitoring and analysis of rumination behavior of dairy cows in group breeding. Full article

(This article belongs to the Special Issue New Technological Applications in Agriculture for the Development of the Circular Bioeconomy)

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19 pages, 5272 KiB

Open AccessFeature PaperArticle

Optimized Design and Experiment of a Self-Covering Furrow Opener for an Automatic Sweet Potato Seedling Transplanting Machine

by Guangwei Wu, Shoujiang Wang, Anqi Zhang, Yuejin Xiao, Liwei Li, Yanxin Yin, Hanqing Li, Changkai Wen and Bingxin Yan

Sustainability 2023, 15(17), 13091; https://doi.org/10.3390/su151713091 - 30 Aug 2023

Cited by 1 | Viewed by 924

Abstract

The yield and quality of sweet potatoes are significantly influenced by the transplantation posture of sweet potato seedlings. The performance of the sweet potato seedling transplanting opener directly affects the transplantation posture of sweet potato seedlings. In order to improve the yield and quality of sweet potatoes, this study proposes a joint simulation method based on discrete element and flexible multi body dynamics (DEM-FMBD), which optimizes the structure of a self-covering soil opener. By exploring the influence of self-covering soil trenchers on the planting depth and posture of sweet potato seedlings during horizontal transplantation, it was determined that the influencing factors of the experiment were wing spacing, soil reflux height, and soil reflux length. Based on the DEM-FMBD coupling simulation platform, single factor, and quadratic rotation orthogonal experiments were carried out. According to the results of the simulation test, the effect of the interaction of test factors on planting depth and planting attitude was analyzed by the response surface method. Finally, the optimal structural parameter combination was obtained by a multi-objective optimization method: the spacing of the wings was 58 mm, the height of the soil backflow port was 71 mm, and the length of the soil backflow port was 163 mm; thus, the quality of transplanting is improved effectively. This study provides the method and theory reference for the study of sweet potato transplanting. Full article

(This article belongs to the Special Issue New Technological Applications in Agriculture for the Development of the Circular Bioeconomy)

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14 pages, 787 KiB

Open AccessEssay

Can Digital Rural Construction Improve China’s Agricultural Surface Pollution? Autoregressive Modeling Based on Spatial Quartiles

by Hanqing Hu, Xiaofan Yang, Jianling Li, Jianbo Shen, Jianhua Dai and Yuanyuan Jin

Sustainability 2023, 15(17), 13138; https://doi.org/10.3390/su151713138 - 31 Aug 2023

Cited by 1 | Viewed by 674

Abstract

The problem of agricultural surface pollution is becoming increasingly prominent, directly impeding the realization of the goals of “industrial prosperity and ecological livability” in the strategy of rural revitalization. To thoroughly analyze the impact of Digital Rural Construction on agricultural surface pollution and to effectively strengthen the prevention and control measures, the Moran index was used to assess the influence of agricultural surface pollution in 31 provinces and cities across China. The Moran index was employed to conduct global and local spatial autocorrelation analysis of agricultural surface source pollution, and a panel quantile autoregressive model was constructed to explore the effects of Digital Rural Construction on such pollution. The results show the following: (1) agricultural surface pollution in each province and city exhibits spatial spillover effects that are growing stronger; (2) the spatial impact of agricultural surface pollution on neighboring provinces and cities follows an inverted U-shaped pattern at different levels of pollution; (3) the relationship between the degree of agricultural surface pollution and the impact of Digital Rural Construction on it also follows an inverted U-shaped pattern, wherein improvements are observed as the pollution levels deepen. When the level of agricultural surface pollution is located in the quartile point 0.1, the improvement effect of Digital Rural Construction on agricultural surface pollution is small (0.0484), as the quartile point increases, the improvement effect is gradually increased, and it reaches the maximum value at the quartile point 0.5 (0.523), and the coefficient of agricultural surface pollution decreases to the minimum value at the quartile point 0.9 (0.423). Full article

(This article belongs to the Special Issue New Technological Applications in Agriculture for the Development of the Circular Bioeconomy)

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