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Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (5 June 2024) | Viewed by 34713

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Special Issue Editors

Prof. Dr. Jin Yuan

E-Mail Website
Guest Editor

School of Mechanical and Electronic Engineering, Shandong Agriculture University, Taian 271018, China
Interests: selective harvesting; robotic manipulator; novel robotic applications; machine vision
Special Issues, Collections and Topics in MDPI journals

Dr. Zichen Huang

E-Mail Website
Co-Guest Editor

Division of Environmental Science and Technology, Kyoto University, Kyoto 606-8502, Japan
Interests: greenhouse robot; positioning system; fluorescence imaging system; food science

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future”, aims to shed light on the cutting-edge innovations and breakthroughs in the realm of smart farming, smart livestock management, and efficient greenhouse administration. The objective is to explore the myriad ways in which these advancements are revolutionizing the agricultural sector, bridging gaps between the rural and urban landscapes, and ensuring food security in an environmentally friendly manner.

Intelligent agricultural machinery and robots are setting the stage for a new era in agriculture. They have dramatically altered traditional practices and introduced groundbreaking concepts such as precision agriculture, automation, and vertical farming. From farm equipment to drones, sensors, and robotics, the landscape of modern agriculture is evolving at an impressive rate. This Special Issue delves into the various pillars of this transformation, focusing on state-of-the-art developments and applications designed to make farming more effective and sustainable.

The Special Issue begins with an overview of smart farming, closely analyzing how AI-driven applications and Internet of Things (IoT) technologies have paved the way for data-driven, customized strategies in agricultural management. Topics covered include remote sensing, GPS technology, and the use of predictive analytics. The benefits and challenges tied to the adoption of smart farming methodologies, including environmental impacts and workforce management, are also discussed.

The Special Issue then transitions to a detailed examination of smart livestock management, highlighting the central role of advanced technologies in monitoring animal health and welfare, enhancing breeding and nutrition programs, and improving overall productivity. By delving into real-world case studies, the Special Issue showcases the remarkable achievements made in livestock farming, while outlining the barriers and ethical challenges that still need to be addressed.

In the exploration of smart greenhouse management, the Special Issue emphasizes the critical part played by automated systems and robotics in maintaining optimal growing conditions, monitoring crop performance, monitoring pest and disease control, and reducing resource consumption. Readers will gain an understanding of how these new-age technologies are boosting quality and yield while minimizing energy use, waste production, and labor requirements.

Lastly, the Special Issue wraps up with a discussion on future trends and research avenues related to intelligent agricultural machinery and robots. It highlights the need for continued research and development in this field, the importance of multi-disciplinary collaboration, and the need to prioritize sustainability and environmental stewardship. With this focus, the Special Issue strives to inspire further innovation and investment in agricultural technology, ultimately fostering agricultural growth and food security for years to come.

Overall, the Special Issue, “Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future”, presents a comprehensive and insightful perspective on cutting-edge agricultural technology. Through diverse contributions and case studies, it offers a rich collection of knowledge, practical insights, and inspiration for researchers, policymakers, practitioners, and stakeholders alike in their journey towards creating a more advanced and sustainable agricultural future.

The aim of this Special Issue on intelligent agricultural machinery and robots is to explore the recent advancements, technologies, and applications in the field of smart farming, smart livestock, and smart greenhouse management. It seeks to provide a platform for researchers, academicians, engineers, and industry experts to share their knowledge, experience, and innovative ideas, and promote interdisciplinary research and development.

The scope of this Special Issue covers a wide range of topics, including, but not limited to:

Intelligent agricultural machinery for precision farming;
Robotics in farming, livestock, and greenhouse management;
Artificial intelligence and machine learning in agriculture;
Smart sensor and IoT applications in agriculture;
Automation in greenhouse management;
Drones and unmanned systems for agricultural applications;
Robotics for harvesting, processing, and grading operations;
Big data analytics in smart agriculture.

Prof. Dr. Jin Yuan
Dr. Zichen Huang
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. Agriculture 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 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

intelligent agricultural machinery
agricultural robotics
smart farming
smart livestock
smart greenhouse management
precision agriculture
automation in agriculture
artificial intelligence in agriculture
IoT and smart sensor in agriculture
drones and unmanned systems
machine learning for crop management
big data analytics
unmanned farming
innovative agricultural machinery

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Published Papers (14 papers)

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19 pages, 5919 KiB

Open AccessArticle

Real-Time ConvNext-Based U-Net with Feature Infusion for Egg Microcrack Detection

by Chenbo Shi, Yuejia Li, Xin Jiang, Wenxin Sun, Changsheng Zhu, Yuanzheng Mo, Shaojia Yan and Chun Zhang

Agriculture 2024, 14(9), 1655; https://doi.org/10.3390/agriculture14091655 - 22 Sep 2024

Viewed by 863

Abstract

Real-time automatic detection of microcracks in eggs is crucial for ensuring egg quality and safety, yet rapid detection of micron-scale cracks remains challenging. This study introduces a real-time ConvNext-Based U-Net model with Feature Infusion (CBU-FI Net) for egg microcrack detection. Leveraging edge features and spatial continuity of cracks, we incorporate an edge feature infusion module in the encoder and design a multi-scale feature aggregation strategy in the decoder to enhance the extraction of both local details and global semantic information. By introducing large convolution kernels and depth-wise separable convolution from ConvNext, the model significantly reduces network parameters compared to the original U-Net. Additionally, a composite loss function is devised to address class imbalance issues. Experimental results on a dataset comprising over 3400 graded egg microcrack image patches demonstrate that CBU-FI Net achieves a reduction in parameters to one-third the amount in the original U-Net, with an inference speed of 21 ms per image (1 million pixels). The model achieves a Crack-IoU of 65.51% for microcracks smaller than 20

μ

m and a Crack-IoU and MIoU of 60.76% and 80.22%, respectively, for even smaller cracks (less than 5

μ

m), achieving high-precision, real-time detection of egg microcracks. Furthermore, on the publicly benchmarked CrackSeg9k dataset, CBU-FI Net achieves an inference speed of 4 ms for 400 × 400 resolution images, with an MIoU of 81.38%, proving the proposed method’s robustness and generalization capability across various cracks and complex backgrounds. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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25 pages, 9183 KiB

Open AccessArticle

A High-Accuracy Contour Segmentation and Reconstruction of a Dense Cluster of Mushrooms Based on Improved SOLOv2

by Shuzhen Yang, Jingmin Zhang and Jin Yuan

Agriculture 2024, 14(9), 1646; https://doi.org/10.3390/agriculture14091646 - 20 Sep 2024

Viewed by 696

Abstract

This study addresses challenges related to imprecise edge segmentation and low center point accuracy, particularly when mushrooms are heavily occluded or deformed within dense clusters. A high-precision mushroom contour segmentation algorithm is proposed that builds upon the improved SOLOv2, along with a contour reconstruction method using instance segmentation masks. The enhanced segmentation algorithm, PR-SOLOv2, incorporates the PointRend module during the up-sampling stage, introducing fine features and enhancing segmentation details. This addresses the difficulty of accurately segmenting densely overlapping mushrooms. Furthermore, a contour reconstruction method based on the PR-SOLOv2 instance segmentation mask is presented. This approach accurately segments mushrooms, extracts individual mushroom masks and their contour data, and classifies reconstruction contours based on average curvature and length. Regular contours are fitted using least-squares ellipses, while irregular ones are reconstructed by extracting the longest sub-contour from the original irregular contour based on its corners. Experimental results demonstrate strong generalization and superior performance in contour segmentation and reconstruction, particularly for densely clustered mushrooms in complex environments. The proposed approach achieves a 93.04% segmentation accuracy and a 98.13% successful segmentation rate, surpassing Mask RCNN and YOLACT by approximately 10%. The center point positioning accuracy of mushrooms is 0.3%. This method better meets the high positioning requirements for efficient and non-destructive picking of densely clustered mushrooms. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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21 pages, 5780 KiB

Open AccessArticle

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

by Meng Wang, Changhe Niu, Zifan Wang, Yongxin Jiang, Jianming Jian and Xiuying Tang

Agriculture 2024, 14(6), 823; https://doi.org/10.3390/agriculture14060823 - 24 May 2024

Cited by 3 | Viewed by 1149

Abstract

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon N_p, control horizon N_c, and preview value N_pre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising N_pre can improve curve tracking performance. N_p was correlated negatively with the curvature change, whereas N_c and N_pre were positively connected. N_p, N_c, and N_pre were correlated positively with the velocity change. Then, the parameters for self-adaptation of N_p, N_c, and N_pre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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18 pages, 5322 KiB

Open AccessArticle

Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images

by Chenbo Shi, Yuanzheng Mo, Xiangqun Ren, Jiahao Nie, Chun Zhang, Jin Yuan and Changsheng Zhu

Agriculture 2024, 14(5), 735; https://doi.org/10.3390/agriculture14050735 - 9 May 2024

Viewed by 1321

Abstract

The segmentation and localization of Agaricus bisporus is a precondition for its automatic harvesting. A. bisporus growth clusters can present challenges for precise localization and segmentation because of adhesion and overlapping. A low-cost image stitching system is presented in this research, utilizing a quick stitching method with disparity correction to produce high-precision panoramic dual-modal fusion images. An enhanced technique called Real-Time Models for Object Detection and Instance Segmentation (RTMDet-Ins) is suggested. This approach utilizes SimAM Attention Module’s (SimAM) global attention mechanism and the lightweight feature fusion module Space-to-depth Progressive Asymmetric Feature Pyramid Network (SPD-PAFPN) to improve the detection capabilities for hidden A. bisporus. It efficiently deals with challenges related to intricate segmentation and inaccurate localization in complex obstacles and adhesion scenarios. The technology has been verified by 96 data sets collected on a self-designed fully automatic harvesting robot platform. Statistical analysis shows that the worldwide stitching error is below 2 mm in the area of 1200 mm × 400 mm. The segmentation method demonstrates an overall precision of 98.64%. The planar mean positioning error is merely 0.31%. The method promoted in this research demonstrates improved segmentation and localization accuracy in a challenging harvesting setting, enabling efficient autonomous harvesting of A. bisporus. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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23 pages, 15534 KiB

Open AccessArticle

Super-Resolution Semantic Segmentation of Droplet Deposition Image for Low-Cost Spraying Measurement

by Jian Liu, Shihui Yu, Xuemei Liu, Guohang Lu, Zhenbo Xin and Jin Yuan

Agriculture 2024, 14(1), 106; https://doi.org/10.3390/agriculture14010106 - 8 Jan 2024

Cited by 2 | Viewed by 1437

Abstract

In-field in situ droplet deposition digitization is beneficial for obtaining feedback on spraying performance and precise spray control, the cost-effectiveness of the measurement system is crucial to its scalable application. However, the limitations of camera performance in low-cost imaging systems, coupled with dense spray droplets and a complex imaging environment, result in blurred and low-resolution images of the deposited droplets, which creates challenges in obtaining accurate measurements. This paper proposes a Droplet Super-Resolution Semantic Segmentation (DSRSS) model and a Multi-Adhesion Concave Segmentation (MACS) algorithm to address the accurate segmentation problem in low-quality droplet deposition images, and achieve a precise and efficient multi-parameter measurement of droplet deposition. Firstly, a droplet deposition image dataset (DDID) is constructed by capturing high-definition droplet images and using image reconstruction methods. Then, a lightweight DSRSS model combined with anti-blurring and super-resolution semantic segmentation is proposed to achieve semantic segmentation of deposited droplets and super-resolution reconstruction of segmentation masks. The weighted IoU (WIoU) loss function is used to improve the segmented independence of droplets, and a comprehensive evaluation criterion containing six sub-items is used for parameter optimization. Finally, the MACS algorithm continues to segment the remained adhesive droplets processed by the DSRSS model and corrects the bias of the individual droplet regions by regression. The experiments show that when the two weight parameters α and β in WIoU are 0.775 and 0.225, respectively, the droplet segmentation independence rate of DSRSS on the DDID reaches 0.998, and the IoU reaches 0.973. The MACS algorithm reduces the droplet adhesion rate in images with a coverage rate of more than 30% by 15.7%, and the correction function reduces the coverage error of model segmentation by 3.54%. The parameters of the DSRSS model are less than 1 M, making it possible to run it on embedded platforms. The proposed approach improves the accuracy of spray measurement using low-quality droplet deposition image and will help to scale-up of fast spray measurements in the field. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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20 pages, 9708 KiB

Open AccessArticle

Design and Validation of a Variable-Rate Control Metering Mechanism and Smart Monitoring System for a High-Precision Sugarcane Transplanter

by Abdallah E. Elwakeel, Yasser S. A. Mazrou, Ahmed S. Eissa, Abdelaziz M. Okasha, Adel H. Elmetwalli, Abeer H. Makhlouf, Khaled A. Metwally, Wael A. Mahmoud and Salah Elsayed

Agriculture 2023, 13(12), 2218; https://doi.org/10.3390/agriculture13122218 - 30 Nov 2023

Cited by 5 | Viewed by 2251

Abstract

The current study aimed to design and test the accuracy of a variable-rate control metering mechanism (VRCMM) and a remote smart monitoring system (RSMS) for a precision sugarcane transplanter based on IoT technology. The VRCMM is used to operate the seedling metering device at different speeds using a stepper motor based on the travel speed, and the RSMS was employed to evaluate of the three basic parameters of seedling amount, optimum rate, and missed rate. Two types of sensors were used for detecting the sugarcane seedling (SS) and travel speed, including one ultrasonic sensor and one infrared RPM sensor. The study was performed at five travel speeds and four transplant spacings. The findings of laboratory tests showed that the mean record of the relative error between the desired stepper motor speed of the VRCMM and the real value was 3.39%, and it increased with increasing the travel speed. as Additionally, the speed regulation performance was in agreement with the transplanting index. The change in RSMS accuracy is obvious when the travel speed is high and the transplant spacing is small. The RSMS accuracy drops sharply, revealing a leaping change. In conclusion, the smart and intelligent designed sugarcane transplanter would be very useful in sugarcane production. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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30 pages, 8878 KiB

Open AccessArticle

Development of Boom Posture Adjustment and Control System for Wide Spray Boom

by Jinyang Li, Zhenyu Nie, Yunfei Chen, Deqiang Ge and Meiqing Li

Agriculture 2023, 13(11), 2162; https://doi.org/10.3390/agriculture13112162 - 17 Nov 2023

Cited by 3 | Viewed by 2166

Abstract

To obtain a more consistent droplet distribution and reduce spray drift, it is necessary to keep the entire spray boom parallel to the crop canopy or ground and maintain a certain distance from the spray nozzles to the crop canopy or ground. A high-performance boom active control system was developed for boom trapezoid suspension. The hydraulic system and hardware circuit of the boom control system were designed based on analyzing the configuration of active trapezoid suspension. The mathematical models of valve-controlled hydraulic cylinders and active boom suspensions were developed. Step response and frequency domain response analysis of passive suspension were conducted by Simulink simulations, and then key parameters of the boom suspension and hydraulic system were determined. A feedforward proportion integration differentiation (FPID) control algorithm was proposed to improve the tracking performance. The designed control system was assembled on a 24 m boom with trapezoid suspension. The response characteristic of the active boom control system was tested by the step signal and the sinusoidal signal from a six-degree-of-freedom hydraulic motion platform. Firstly, the tracking performance of the active balance control system for the PID (proportion integration differentiation) and FPID control algorithms was compared for a given 0.2 Hz sine signal. Then, for the ground-following control system, the response characteristics in challenging terrain and tracking performance in less challenging terrain were tested. Field experiment results indicate that the maximum rolling angle of the chassis was

3.896 °

while the maximum inclination angle of the boom was

0.453 °

. The results show that the designed boom adjustment and control system can effectively adjust the boom motion in real time and meet the requirements of field operation. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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20 pages, 11140 KiB

Open AccessArticle

Embedded Field Stalk Detection Algorithm for Digging–Pulling Cassava Harvester Intelligent Clamping and Pulling Device

by Wang Yang, Junhui Xi, Zhihao Wang, Zhiheng Lu, Xian Zheng, Debang Zhang and Yu Huang

Agriculture 2023, 13(11), 2144; https://doi.org/10.3390/agriculture13112144 - 14 Nov 2023

Cited by 2 | Viewed by 1583

Abstract

Cassava (Manihot esculenta Crantz) is a major tuber crop worldwide, but its mechanized harvesting is inefficient. The digging–pulling cassava harvester is the primary development direction of the cassava harvester. However, the harvester clamping–pulling mechanism cannot automatically adjust its position relative to the stalks in forward movement, which results in clamping stalks with a large off-center distance difficulty, causing large harvest losses. Thus, solving the device’s clamping location problem is the key to loss reduction in the harvester. To this end, this paper proposes a real-time detection method for field stalks based on YOLOv4. First, K-means clustering is applied to improve the consistency of cassava stalk detection boxes. Next, the improved YOLOv4 network’s backbone is replaced with MobileNetV2 + CA, resulting in the KMC-YOLO network. Then, the proposed model’s validity is demonstrated using ablation studies and comparison tests. Finally, the improved network is embedded into the NVIDIA Jetson AGX Xavier, and the model is accelerated using TensorRT, before conducting field trials. The results indicate that the KMC-YOLO achieves average precision (AP) values of 98.2%, with detection speeds of 33.6 fps. The model size is reduced by 53.08% compared with the original YOLOv4 model. The detection speed after TensorRT acceleration is 39.3 fps, which is 83.64% faster than before acceleration. Field experiments show that the embedded model detects more than 95% of the time at all three harvest illumination levels. This research contributes significantly to the development of cassava harvesters with intelligent harvesting operations. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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23 pages, 6971 KiB

Open AccessArticle

Motion-Control Strategy for a Heavy-Duty Transport Hexapod Robot on Rugged Agricultural Terrains

by Kuo Yang, Xinhui Liu, Changyi Liu and Ziwei Wang

Agriculture 2023, 13(11), 2131; https://doi.org/10.3390/agriculture13112131 - 11 Nov 2023

Cited by 2 | Viewed by 1949

Abstract

Legged agricultural transportation robots are efficient tools that can autonomously transport goods over agricultural terrain, and their introduction helps to improve the efficiency and quality of agricultural production. Their effectiveness depends on their adaptability to different environmental conditions, which is especially true for heavy-duty robots that exert ground forces. Therefore, this study proposes a motion-control strategy for a heavy-duty transport hexapod robot. Two critical tasks were accomplished in this paper: (1) estimating the support surface angle based on the robot’s foot position and body posture, and accordingly determining the motion constraint conditions on this support surface and the body posture based on energy optimization; (2) proposing an adaptive fuzzy impedance algorithm for real-time force–position composite control for adjusting foot position, in order to reduce the steady-state force tracking error caused by terrain stiffness, thus ensuring body stability through tracking of variable foot-end forces. An element of hardware in the loop control platform for a 3.55-ton device was designed and compared with the current popular force-control methods under different external contact terrains. The results show that the proposed control method can effectively reduce force errors, establish support forces faster on less-stiff environments, and reduce the torso tilt during phase switching. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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13 pages, 2282 KiB

Open AccessArticle

Drivable Agricultural Road Region Detection Based on Pixel-Level Segmentation with Contextual Representation Augmentation

by Yefeng Sun, Liang Gong, Wei Zhang, Bishu Gao, Yanming Li and Chengliang Liu

Agriculture 2023, 13(9), 1736; https://doi.org/10.3390/agriculture13091736 - 1 Sep 2023

Cited by 1 | Viewed by 1441

Abstract

Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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16 pages, 5507 KiB

Open AccessArticle

Optimal Sand−Paving Parameters Determination of an Innovatively Developed Automatic Maize Seeding Machine

by Bohan Fu, Weizhong Sun and Zhao Zhang

Agriculture 2023, 13(8), 1538; https://doi.org/10.3390/agriculture13081538 - 2 Aug 2023

Viewed by 1341

Abstract

Maize is an important crop to ensure food safety. High-quality seeds can guarantee a good yield. The maize seed germination rate is the most important information for the maize industry, which can be obtained through the seed germination test. An essential stage in determining the germination rate is the planting of the seeds. The current seed planting process is fully manual, which is labor-intensive and costly, and it requires the development of an autonomous seeding machine. This research developed an automatic maize seeding machine, consisting of four operations: paving sand, seed layout, watering, and covering the seed. Among the four procedures, sand paving is a crucial step, the performance of which is affected by the gate opening size, conveyor speed, and sensor mounting location. Three performance evaluating factors are the weight of sand in the tray, the volume of sand left on the conveyor, and sand surface flatness. A full factorial experiment was designed with three variables and three levels to determine an appropriate factor combination. RGB-D information was used to calculate the volume of sand left on the conveyor and sand flatness. An analytic hierarchy process was employed to assign weights to the three evaluation indicators and score the various combinations of factors. The machine for paving sand achieved a satisfactory result with an opening size of 10.8 mm, a sensor distance of 9 cm, and a conveyor belt speed of 5.1 cm/s. With the most satisfactory factors determined, the machine shows superior performance to better meet practical applications. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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Review

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20 pages, 6988 KiB

Open AccessReview

Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields

by Wenbo Wei, Maohua Xiao, Weiwei Duan, Hui Wang, Yejun Zhu, Cheng Zhai and Guosheng Geng

Agriculture 2024, 14(9), 1473; https://doi.org/10.3390/agriculture14091473 - 29 Aug 2024

Viewed by 1988

Abstract

Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture is increasingly confronted with a labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture. Nevertheless, significant challenges persist in the practical application of this technology, particularly concerning adaptability, operational precision, and efficiency. This review seeks to systematically explore the advancements in unmanned agricultural operations, with a focus on onboard environmental sensing, full-coverage path planning, and autonomous operational control technologies. Additionally, this review discusses the challenges and future directions of key technologies for the autonomous operation of agricultural equipment in large fields. This review aspires to serve as a foundational reference for the development of autonomous operation technologies for large-scale agricultural equipment. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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28 pages, 1512 KiB

Open AccessReview

The Application of Artificial Intelligence Models for Food Security: A Review

by Rebecca Sarku, Ulfia A. Clemen and Thomas Clemen

Agriculture 2023, 13(10), 2037; https://doi.org/10.3390/agriculture13102037 - 23 Oct 2023

Cited by 5 | Viewed by 8963

Abstract

Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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19 pages, 789 KiB

Open AccessReview

Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review

by Wei Liu, Shijie Tian, Qingyu Wang and Huanyu Jiang

Agriculture 2023, 13(8), 1488; https://doi.org/10.3390/agriculture13081488 - 27 Jul 2023

Cited by 13 | Viewed by 3623

Abstract

The process of plug tray seedling transplanting is a crucial step in protected agriculture production. Due to issues such as high labor intensity, poor consistency of work quality, and low efficiency, the application of automated transplanting machines has provided a solution to these issues. For the diversity of transplanting operations, various mechanical structures and technological applications have been developed for automated transplanting equipment. Therefore, this paper provides systematic research of current studies on the key transplanter technologies. Firstly, through an analysis of the types of transplanting operations, the technical requirements of automated transplanting equipment for different operation types are elucidated. Subsequently, the key technologies applied in transplanting machines are discussed from the perspectives of substrate physical characteristics, end effectors, integration of multiple end effectors, vision systems, and transplanting path planning. Moreover, an analysis is conducted on the advantages, disadvantages, and application scenarios of different research methods for each key technology. Lastly, the existing problems and technical difficulties of the transplanting machine are summarized, and future research directions are discussed. This analysis provides a valuable reference for further research and development in the field of transplanting machines for plug tray seedlings. Full article

(This article belongs to the Special Issue Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future)

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Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future

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