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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 April 2025 | Viewed by 9991

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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

Department of Smart Agriculture and Engineering, Wenzhou Vocational College of Science and Technology, Wenzhou 325006, 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

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Keywords

agricultural circular economy
digital villages
new technologies
new methods
new equipment

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Published Papers (7 papers)

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Research

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14 pages, 7671 KiB

Open AccessArticle

Multiscale Tea Disease Detection with Channel–Spatial Attention

by Yange Sun, Mingyi Jiang, Huaping Guo, Li Zhang, Jianfeng Yao, Fei Wu and Gaowei Wu

Sustainability 2024, 16(16), 6859; https://doi.org/10.3390/su16166859 - 9 Aug 2024

Viewed by 730

Abstract

Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the backbone network and fuse these features through the neck for accurate disease detection. This paper proposes a novel tea disease detection method that enhances feature expression of the backbone network and the feature fusion capability of the neck: (1) constructing an inverted residual self-attention module as a backbone plugin to capture the long-distance dependencies of disease spots on the leaves; and (2) developing a channel–spatial attention module with residual connection in the neck network to enhance the contextual semantic information of fused features in disease images and eliminate complex background noise. For the second step, the proposed channel–spatial attention module uses Residual Channel Attention (RCA) to enhance inter-channel interactions, facilitating discrimination between disease spots and normal leaf regions, and employs spatial attention (SA) to enhance essential areas of tea diseases. Experimental results demonstrate that the proposed method achieved accuracy and mAP scores of 92.9% and 94.6%, respectively. In particular, this method demonstrated improvements of 6.4% in accuracy and 6.2% in mAP compared to the SSD model. 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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13 pages, 1136 KiB

Open AccessArticle

Response of Stand Spatial Structure to Nitrogen Addition in Deciduous Broad-Leaved Forest in Jigong Mountain

by Liang Hong, Guangshuang Duan, Shenglei Fu, Liyong Fu, Lei Ma, Xiaowei Li and Juemin Fu

Sustainability 2024, 16(12), 5137; https://doi.org/10.3390/su16125137 - 17 Jun 2024

Viewed by 779

Abstract

Significant influences on tree growth and forest functionality are attributed to nitrogen (N) addition. However, limited research has been conducted on the effects of N addition on forest spatial structure. In this study, we examined the effects of different N addition methods and concentrations on the stand spatial structure of a deciduous broad-leaved forest over the period 2012 to 2017. Five N addition treatments were implemented: CK (control group without N addition), CN25 (low N concentration added to the canopy), CN50 (high N concentration added to the canopy), UN25 (low N concentration added to the understory), and UN50 (high N concentration added to the understory). The results showed a moderate influence of N addition (CN25, CN50, UN25, UN50) on optimizing the stand spatial structure. CN25, CN50, and UN25 increased the mean values of the mingling degree (M) and neighborhood comparison (U), while decreasing the mean value of the uniform angle index (W), although these effects were not significant. Enhancements in the average value of the crowding degree (C) and comprehensive spatial structure index (CSSI) between 2012 and 2017 were found in all five treatments, demonstrating statistical significance. Assessing the distribution of the stand spatial structure index, CN25, CN50, and UN25 increased the proportion of M at an intensity (M = 0.75) and extreme intensity (M = 1), while decreasing the proportion at zero intensity (M = 0), weak intensity (M = 0.25), and moderate intensity (M = 0.5). A decrease in the proportion of trees was noted when U = 0 (excluding UN50), with no discernible pattern found in the frequency distribution of other values. CN50 and UN25 increased the proportion of W at a moderate level (W = 0.5), while CN25 and UN50 reduced it. No clear pattern was detected in the frequency distributions of other values. All five treatments increased the proportion of C at the maximum level (C = 1), while decreasing the proportions at levels of 0, 0.25, and 0.5 in 2017. Intriguingly, nitrogen addition treatments appeared to optimize the stand spatial structure to some extent and stimulated the growth of trees with larger diameters. Nevertheless, the short duration of the data collection period, spanning only five years, may have influenced the significance of the outcomes, underlining the requirement for extended studies. Conclusively, N deposition adjusted and enhanced the stand spatial structure to various degrees within the research region, providing valuable insights for further optimization of forest management. 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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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

Cited by 2 | Viewed by 1618

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 1636

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 4 | Viewed by 1396

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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23 pages, 7344 KiB

Open AccessReview

Application of Decentralized Wastewater Treatment Technology in Rural Domestic Wastewater Treatment

by Xinyu Li, Xu Zhang, Min Zhao, Xiangyong Zheng, Zhiquan Wang and Chunzhen Fan

Sustainability 2024, 16(19), 8635; https://doi.org/10.3390/su16198635 - 5 Oct 2024

Viewed by 1373

Abstract

The management of domestic wastewater in rural areas has always been challenging due to characteristics such as the wide distribution and dispersion of rural households. There are numerous domestic sewage discharge methods used in rural areas, and it is difficult to treat the sewage. To address this problem, decentralized wastewater treatment systems (DWTSs) have been installed around the globe to reuse and recycle wastewater for non-potable uses such as firefighting, toilet flushing, and landscape irrigation. This study compares the currently implemented treatment processes by investigating them from the point of view of their performance and their advantages and disadvantages to provide new ideas for the development of rural wastewater treatment technologies. According to conventional treatment technologies including activated sludge (OD, A/O, A/A/O, SBR), biofilm (biofilter, MBBR, biological contact oxidation, biofluidized bed) and biogas digesters, natural biological treatment technologies including artificial wetlands (surface flow, vertical flow, horizontal submerged flow artificial wetlands), soil percolation systems (slow, fast, subsurface percolation and surface diffusion) and stabilization pond technology and combined treatment technologies are categorized and further described. 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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Other

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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 1059

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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