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Biomimetics
Special Issues
Biomimetics in Agri-Food: From Preliminary Design to Field Applications
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Biomimetics in Agri-Food: From Preliminary Design to Field Applications

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Development of Biomimetic Methodology".

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 6143

Special Issue Editors

Dr. Francesco Visentin

E-Mail Website
Guest Editor
Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
Interests: soft robotics; material for soft robots; soft sensors; AI/ML; bio-inspired robotics
Prof. Dr. Junzhi Yu

E-Mail Website
Guest Editor
Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
Interests: intelligent robots; advanced robot control; embedded vision
Special Issues, Collections and Topics in MDPI journals
Dr. Ali Leylavi Shoushtari

E-Mail Website
Guest Editor Assistant
Farm Technology Group, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
Interests: agricultural robotics; soft robotics; soft grippers

Special Issue Information

Dear Colleagues,

Nature has evolved diverse and versatile structures and mechanisms to deal with variations and uncertainties in the environment. This includes variations in shape, size, mechanical properties, and other factors that can affect their ability to survive and thrive in different environments. Agriculture is also subject to high variations and uncertainties, making it a complex and challenging task. Traditional agricultural machinery often lacks the versatility to deal with these challenges effectively, resulting in lower productivity and efficiency. By drawing inspiration from the complex and adaptable structures found in nature, engineers can develop versatile robots and machines that can address these challenges. For example, soft robots can adapt to the shape and size of different crops, improving precision and accuracy in tasks such as planting and harvesting. Other examples include robots that can mimic animal and plant behaviors to move efficiently over and through the soil to measure nutrient availability, sensory organs to capture information on crops, or peculiar adhesion mechanisms to improve the manipulation in harvesting.

This Special Issue calls for contributions for the application in the agri-food field to discuss to what extent the bio-inspired approach can offer favorable impacts in this fast-growing and high-demanding field. We encourage the submission of experimental papers, practical applications, and reviews on the specific aspects of the agri-food field.

Dr. Francesco Visentin
Prof. Dr. Junzhi Yu
Guest Editors

Dr. Ali Leylavi Shoushtari
Guest Editor Assistant

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. Biomimetics is an international peer-reviewed open access monthly 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 2200 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

  • hybrid design
  • soft robotics
  • bio-inspired grippers
  • bio-inspired locomotion
  • bio-inspired sensing and perception
  • nature-inspired planning and control
  • biomimetic design

Published Papers (5 papers)

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Research

19 pages, 11291 KiB  
Article
A Bionic Walking Wheel for Enhanced Trafficability in Paddy Fields with Muddy Soil
by Duo Chen, Yan Xu, Yuqiu Song, Mingjin Xin, Liyan Wu, Aiju Kong, Huan Wang, Pengchao Dai and Hongpeng Yu
Biomimetics 2024, 9(2), 68; https://doi.org/10.3390/biomimetics9020068 - 24 Jan 2024
Cited by 1 | Viewed by 1015
Abstract
To improve wheel trafficability in soft and muddy soils such as paddy fields, a bionic walking wheel is designed based on the structural morphology and movement mode of the feet of waders living in marshes and mudflats, similar to the muddy soil of [...] Read more.
To improve wheel trafficability in soft and muddy soils such as paddy fields, a bionic walking wheel is designed based on the structural morphology and movement mode of the feet of waders living in marshes and mudflats, similar to the muddy soil of paddy fields. The bionic walking wheel adopts the arrangement of double-row wheel legs and staggered arrays to imitate the walking posture of waders. The two legs move alternately, cooperate with each other, and improve the smoothness of movement. The cam inside the bionic walking wheel is used to control the movement mode of the feet. The flippers open before touching the ground to increase the contact area and reduce sinking, and the toes bend and grip the ground while touching the ground to increase traction. Multi-rigid-body dynamics software (Adams View 2020) is used to simulate the movement of the wheel during the wading process, and the movement coordination and interference between the wheel legs are analyzed. The simulation results show that there is no interference between the parts and that the movement smoothness is good. The interaction between the bionic walking wheel and muddy soil was analyzed via coupled EDEM–ADAMS simulation, and the simulation analysis and experiments were conducted and compared with those for a common paddy wheel. The results showed that the bionic walking wheel designed in this paper improved the drawbar pull by 113.56% compared with that of a common paddy wheel and had better anti-sinking performance. By analyzing the effect of toe grip on traction, it was found that the soil under the feet can be disturbed to provide greater traction when the toe is bent downward. This study provides a reference for improving the trafficability of walking mechanisms in soft and muddy soils, such as paddy fields. Full article
(This article belongs to the Special Issue Biomimetics in Agri-Food: From Preliminary Design to Field Applications)
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14 pages, 4360 KiB  
Article
Performance Evaluation of a Cicada-Inspired Subsoiling Tool Using DEM Simulations
by Xuezhen Wang, Ruizhi Du, Lingxin Geng, Hanmi Zhou and Jiangtao Ji
Biomimetics 2024, 9(1), 25; https://doi.org/10.3390/biomimetics9010025 - 3 Jan 2024
Viewed by 1066
Abstract
Subsoiling practice is an essential tillage practice in modern agriculture. Tillage forces and energy consumption during subsoiling are extremely high, which reduces the economic benefits of subsoiling technology. In this paper, a cicada-inspired biomimetic subsoiling tool (CIST) was designed to reduce the draught [...] Read more.
Subsoiling practice is an essential tillage practice in modern agriculture. Tillage forces and energy consumption during subsoiling are extremely high, which reduces the economic benefits of subsoiling technology. In this paper, a cicada-inspired biomimetic subsoiling tool (CIST) was designed to reduce the draught force during subsoiling. A soil–tool interaction model was developed using EDEM and validated using lab soil bin tests with sandy loam soil. The validated model was used to optimize the CIST and evaluate its performance by comparing it with a conventional chisel subsoiling tool (CCST) at various working depths (250–350 mm) and speeds (0.5–2.5 ms−1). Results showed that both simulated draught force and soil disturbance behaviors agreed well with those from lab soil bin tests, as indicated by relative errors of <6.1%. Compared with the CCST, the draught forces of the CIST can be reduced by 17.7% at various working depths and speeds; the design of the CIST obviously outperforms some previous biomimetic designs with largest draught force reduction of 7.29–12.8%. Soil surface flatness after subsoiling using the CIST was smoother at various depths than using the CCST. Soil loosening efficiencies of the CIST can be raised by 17.37% at various working speeds. Results from this study implied that the developed cicada-inspired subsoiling tool outperforms the conventional chisel subsoiling tool on aspects of soil disturbance behaviors, draught forces, and soil loosening efficiencies. This study can have implications for designing high-performance subsoiling tools with reduced draught forces and energy requirements, especially for the subsoiling tools working under sandy loam soil. Full article
(This article belongs to the Special Issue Biomimetics in Agri-Food: From Preliminary Design to Field Applications)
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17 pages, 5328 KiB  
Article
Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification
by Ahmed S. Almasoud, Hanan Abdullah Mengash, Muhammad Kashif Saeed, Faiz Abdullah Alotaibi, Kamal M. Othman and Ahmed Mahmud
Biomimetics 2023, 8(7), 535; https://doi.org/10.3390/biomimetics8070535 - 10 Nov 2023
Viewed by 1201
Abstract
Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant [...] Read more.
Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques. Full article
(This article belongs to the Special Issue Biomimetics in Agri-Food: From Preliminary Design to Field Applications)
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23 pages, 12088 KiB  
Article
Design and Experiment of a Biomimetic Duckbill-like Vibration Chain for Physical Weed Control during the Rice Tillering Stage
by Longyu Fang, Xiwen Luo, Zaiman Wang, Wenwu Yang, Hui Li, Shiyu Song, Haoyang Xie, Jianhao Hu, Weiman Chen and Qinghai Liu
Biomimetics 2023, 8(5), 430; https://doi.org/10.3390/biomimetics8050430 - 18 Sep 2023
Viewed by 1196
Abstract
The widespread use of chemical herbicides has jeopardized concerns about food safety and ecological consequences. To address these issues and reduce reliance on chemical herbicides, a physical weed control device was developed for the tillering stage in paddy fields. This device features a [...] Read more.
The widespread use of chemical herbicides has jeopardized concerns about food safety and ecological consequences. To address these issues and reduce reliance on chemical herbicides, a physical weed control device was developed for the tillering stage in paddy fields. This device features a biomimetic duckbill-like vibration chain that effectively controls weed outbreaks. The chain penetrates the soft surface soil of the paddy field under gravity and rapidly stirs the soil through vibration, leading to the detachment of the weed roots anchored in the surface layer. Simultaneously, the device avoids mechanical damage to rice seedlings rooted in deeper soil. This study aimed to investigate the effects of chain structural parameters (the number of chain rows, vibration amplitude, and length of chains) and operational parameters (vibration frequency and working velocity) on weed control efficiency and rice seedling damage. Through a central composite regression field test, the optimal device structure and operational parameters were determined. The optimization results demonstrated that a vibration amplitude of 78.8 mm, a chain length of 93.47 cm, and 3.4 rows of chains, along with a vibration frequency and working velocity ranging from 0.5 to 1.25 m/s, achieved an optimal weeding effect. Under the optimal parameter combination, field test results demonstrated that approximately 80% of the weeds in the field were effectively cleared. This indicates that the design of the biomimetic duckbill-like vibration chain weeding device exhibits a relatively superior weeding performance, offering a practical solution for the management of weeds in rice fields. Full article
(This article belongs to the Special Issue Biomimetics in Agri-Food: From Preliminary Design to Field Applications)
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22 pages, 4983 KiB  
Article
RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection
by Omneya Attallah
Biomimetics 2023, 8(5), 417; https://doi.org/10.3390/biomimetics8050417 - 7 Sep 2023
Viewed by 1060
Abstract
Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to solving this issue and dealing with the dearth [...] Read more.
Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to solving this issue and dealing with the dearth of subject-matter specialists in this area. Nonetheless, a major generalization obstacle is posed by the existence of small discrepancies between various classes of paddy diseases. Numerous studies have used features taken from a single deep layer of an individual complex DL construction with many deep layers and parameters. All of them have relied on spatial knowledge only to learn their recognition models trained with a large number of features. This study suggests a pipeline called “RiPa-Net” based on three lightweight CNNs that can identify and categorize nine paddy diseases as well as healthy paddy. The suggested pipeline gathers features from two different layers of each of the CNNs. Moreover, the suggested method additionally applies the dual-tree complex wavelet transform (DTCWT) to the deep features of the first layer to obtain spectral–temporal information. Additionally, it incorporates the deep features of the first layer of the three CNNs using principal component analysis (PCA) and discrete cosine transform (DCT) transformation methods, which reduce the dimension of the first layer features. The second layer’s spatial deep features are then combined with these fused time-frequency deep features. After that, a feature selection process is introduced to reduce the size of the feature vector and choose only those features that have a significant impact on the recognition process, thereby further reducing recognition complexity. According to the results, combining deep features from two layers of different lightweight CNNs can improve recognition accuracy. Performance also improves as a result of the acquired spatial–spectral–temporal information used to learn models. Using 300 features, the cubic support vector machine (SVM) achieves an outstanding accuracy of 97.5%. The competitive ability of the suggested pipeline is confirmed by a comparison of the experimental results with findings from previously conducted research on the recognition of paddy diseases. Full article
(This article belongs to the Special Issue Biomimetics in Agri-Food: From Preliminary Design to Field Applications)
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