Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Robotic Systems for Future Farming

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 5099

Share This Special Issue

Special Issue Editors

Dr. Xiaojun Jin

E-Mail Website
Guest Editor

National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence; machine vision; robotics
Special Issues, Collections and Topics in MDPI journals

Dr. Dong Chen

E-Mail Website
Guest Editor

Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: agricultural robotics; control; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture faces significant challenges globally, such as labor shortages, environmental sustainability, food security, and resource efficiency. Robotic systems integrated with advanced sensor technologies offer transformative solutions, reshaping traditional farming practices into precision-driven, sustainable, and highly productive systems. The synergy between robotics and sensors enables enhanced crop monitoring, automated pest and disease detection, precise application of inputs, and improved decision-making capabilities through data-driven insights.

This Special Issue invites original research and comprehensive reviews highlighting recent developments, innovative technologies, and practical applications of robotic systems specifically designed for modern and future agricultural practices. Submissions should emphasize sensor integration, sensing methodologies, data processing, and practical implementation of robotic solutions in farming.

Potential topics include, but are not limited to, the following:

Robotics for precision agriculture;
Sensor-integrated autonomous agricultural vehicles;
AI-enabled robotic sensing systems;
Multi-sensor data fusion for farming robots;
Vision-based robotic systems for plant monitoring;
Robotic harvesting systems;
Agricultural robot navigation and localization;
Environmental sensing for automated farming;
Smart sensor networks in robotic agriculture;
IoT-enabled robotic management of farms.

Dr. Xiaojun Jin
Dr. Dong Chen
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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 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

robotic farming
precision agriculture
agricultural robotics
AI-enabled sensors
sensor fusion
autonomous systems
smart farming
environmental sensing
IoT in agriculture
agricultural automation

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

29 pages, 15472 KB

Open AccessArticle

DB-LIO: Database-Driven LiDAR–Inertial Odometry for Memory-Bounded Persistent Mapping

by Hun-Hee Kim, Ho-Hyun Kang, Dong-Hee Noh and Hea-Min Lee

Sensors 2026, 26(10), 3061; https://doi.org/10.3390/s26103061 - 12 May 2026

Viewed by 365

Abstract

This paper proposes DB-LIO (database-driven LiDAR-inertial odometry), a simultaneous localization and mapping (SLAM) system that addresses memory scalability challenges in extended autonomous operation. Existing LiDAR-SLAM systems accumulate keyframe history in memory, leading to

O (N)

growth and out-of-memory failures during extended [...] Read more.

O (N)

growth and out-of-memory failures during extended operation. To overcome this limitation, DB-LIO introduces three core design elements. First, it proposes a spatially indexed keyframe management scheme that persistently stores keyframes in SQLite with R-Tree spatial indexing, enabling

O (\log N + k)

spatial queries that tightly couple cache eviction with factor-graph optimization requirements—a design that ensures every keyframe potentially involved in the next optimization cycle resides in cache. Second, it presents a four-level memory bounding architecture—SLAM-engine keyframe trimming with transparent on-demand reloading, a DB-level least recently used (LRU) cache with a spatial active window, Scan Context descriptor pool bounding, and iSAM2 sliding window compaction with a sparse global anchor graph—that collectively bounds the dominant memory consumers to

O (C)

. Third, the DB-based persistent storage enables a localization mode that can reload previously built maps—including full point clouds, six-degree-of-freedom poses, timestamps, and inter-keyframe relationships—and perform pose estimation using the stored map, which is particularly valuable for agricultural robots and other autonomous systems requiring map reuse. Experiments on a custom orchard dataset demonstrate an 81.9% reduction in memory usage compared with that of the in-memory baseline (2888 MB → 524 MB), while preserving equivalent trajectory accuracy (absolute trajectory error (ATE) root mean square error (RMSE) 0.305 ± 0.001 m vs. 0.296 m). Validation on the KITTI odometry benchmark confirms that the proposed localization mode generalizes across different LiDAR types (Livox Mid360, Velodyne HDL-64E) and environments (orchard, urban driving). Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

► Show Figures

Figure 1

16 pages, 3496 KB

Open AccessArticle

A Four-Wavelength Flow-Through Fluorescence–Scatterometric Sensor That Allows for Real-Time Determination of Fat and Protein Content in Milk–Air Mixtures with High Accuracy

by Maxim E. Astashev, Dmitry N. Ignatenko, Elena A. Molkova, Ivan M. Gogolev, Andrey V. Onegov, Sergey Y. Smolentsev, Artem R. Khakimov, Semen S. Ruzin, Dmitry A. Budnikov, Dmitriy Yu. Pavkin and Sergey V. Gudkov

Sensors 2026, 26(9), 2894; https://doi.org/10.3390/s26092894 - 5 May 2026

Viewed by 1071

Abstract

(1) Background: Currently, there is a problem of prompt determination of fat and protein content in the milk–air mixture of milking machines. (2) Methods: A design of a sensor prototype is proposed, combining measurements of light scattering (scatterometry) and fluorescence (fluorometry) to determine the component composition of the milk–air mixture formed during milking. (3) Results: An optical and electronic circuit of a flow sensor has been developed, using four sources of optical radiation: blue, green and red semiconductor lasers (light scattering in milk) and a UV LED (milk fluorescence), as well as an axial photodiode array for recording the light scattering indicatrix and the fluorescence intensity of the milk–air mixture. The use of three laser sources in the scatterometric circuit allows for the determination of the fat content in milk with an error of 0.05%, which is better than all currently known analogs. The developed sensor enables the detection of counterfeit milk containing palm oil instead of milk fat. It operates reliably in a temperature range of 5–35 °C and at milk flow rates of up to 100 mL/sec. (4) Conclusions: The sensor is capable of transmitting real-time data on the fat and protein content of milk to an RS-232 serial port, enabling integration into milking robots and automated milking systems. Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

► Show Figures

Figure 1

27 pages, 2135 KB

Open AccessArticle

Optimization of Farmland Cultivated Land Path Based on Hybrid Adaptive Neighborhood Search Algorithm

by Han Lv, Zhixin Yao and Taihong Zhang

Sensors 2026, 26(4), 1202; https://doi.org/10.3390/s26041202 - 12 Feb 2026

Viewed by 495

Abstract

Path planning for large-scale agricultural fields faces challenges such as irregular field shapes, uncertain boundaries, and the need to balance path efficiency, energy consumption, and coverage quality. To address these problems, this research introduces a strategy-aware hierarchical hybrid optimization framework (HANS) for autonomous agricultural operations. This framework introduces a global principal axis extraction method based on Principal Component Analysis (PCA), utilizing the statistical distribution of field boundaries to guide path direction, thereby improving robustness against boundary noise and irregular geometries. The framework integrates Adaptive Large Neighborhood Search (ALNS) for global exploration and Tabu Search (TS) for local optimization, forming a tightly coordinated hybrid structure. The framework further employs a Pareto-set-based decision support selection strategy to solve a multi-objective optimization model encompassing machine kinematics, turning patterns, and energy-aware cost evaluation. This strategy provides three methods: weighted preference-based compromise solution selection, crowding distance-based diversified solution selection, and single-objective extreme value-based dedicated optimization solution selection. To balance the impact of path length, energy consumption, and coverage rate, we assigned equal or nearly equal weights to them (i.e., (0.33, 0.33, 0.34)). Furthermore, the framework incorporates operators and feedback learning mechanisms specific to agricultural coverage path problems to enable adaptive operator selection and reduce reliance on manual parameter tuning. Simulation results under three representative field scenarios show that compared to fixed-direction planning, HANS improves the average coverage rate by 0.51 percentage points and reduces fuel consumption by 4.34%. Compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), and Simulated Annealing (SA), the proposed method shortens the working path length by 0.37–0.83%, improves coverage rate by 0.34–1.11%, and reduces energy consumption by 0.61–1.03%, while maintaining competitive computational costs. These results demonstrate the effectiveness and practicality of HANS in large-scale autonomous farming operations. Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

► Show Figures

Figure 1

20 pages, 4952 KB

Open AccessArticle

Star Lightweight Convolution and NDT-RRT: An Integrated Path Planning Method for Walnut Harvesting Robots

by Xiangdong Liu, Xuan Li, Bangbang Chen, Jijing Lin, Kejia Zhuang and Baojian Ma

Sensors 2026, 26(1), 305; https://doi.org/10.3390/s26010305 - 2 Jan 2026

Viewed by 842

Abstract

To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model’s lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots. Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

► Show Figures

Figure 1

28 pages, 5802 KB

Open AccessArticle

An Autonomous Operation Path Planning Method for Wheat Planter Based on Improved Particle Swarm Algorithm

by Shuangshuang Du, Yunjie Zhao, Yongqiang Tian and Taihong Zhang

Sensors 2025, 25(17), 5468; https://doi.org/10.3390/s25175468 - 3 Sep 2025

Cited by 1 | Viewed by 1213

Abstract

To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, the proposed method introduces a Tent chaotic mapping initialization mechanism, a Logistic-based dynamic inertia weight adjustment strategy, and adaptive Gaussian perturbation optimization to achieve precise control of the agricultural machinery’s driving orientation angle. A comprehensive path planning model is constructed with the objectives of minimizing the effective operation path length, reducing turning frequency, and maximizing coverage rate. Furthermore, cubic Bézier curves are employed for path smoothing, effectively controlling path curvature and ensuring the safety and stability of agricultural operations. The simulation experiment results demonstrate that the TLG-PSO algorithm achieved exceptional full-coverage operation performance across four categories of typical test fields. Compared to conventional fixed-direction path planning strategies, the algorithm reduced average total path length by 6228 m, improved coverage rate by 1.31%, achieved average labor savings of 96.32%, and decreased energy consumption by 6.45%. In large-scale comprehensive testing encompassing 1–27 field plots, the proposed algorithm reduced average total path length by 8472 m (a 5.45% decrease) and achieved average energy savings of 44.21 kW (a 5.48% reduction rate). Comparative experiments with mainstream intelligent optimization algorithms, including GA, ACO, PSO, BreedPSO, and SecPSO, revealed that TLG-PSO reduced path length by 0.16%–0.74% and decreased energy consumption by 0.53%–2.47%. It is worth noting that for large-scale field operations spanning hundreds of acres, even an approximately 1% path reduction translates to substantial fuel and operational time savings, which holds significant practical implications for large-scale agricultural production. Furthermore, TLG-PSO demonstrated exceptional performance in terms of algorithm convergence speed and computational efficiency. The improved TLG-PSO algorithm provides a feasible and efficient solution for autonomous operation of large-scale agricultural machinery. Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

► Show Figures

Figure 1

Review

Jump to: Research

30 pages, 2075 KB

Open AccessReview

A Review of Robotic Weeding Modalities for Site-Specific Weed Management

by Feng Gao, Shugui Ding, Wenpeng Zhu, Kang Han, Bin Wu, Maocheng Zhao, Zhong Li and Xiaojun Jin

Sensors 2026, 26(10), 2925; https://doi.org/10.3390/s26102925 - 7 May 2026

Viewed by 501

Abstract

Weed control remains a critical challenge in modern crop production, particularly under increasing pressure to reduce chemical inputs and improve environmental sustainability. Recent advances in precision agriculture and robotic systems have enabled site-specific weed management, where interventions are applied selectively based on detected weed locations. While extensive research has focused on improving weed detection algorithms, comparatively less attention has been paid to the characteristics and constraints of different weeding modalities, which ultimately determine field performance. This review presents a systematic analysis of robotic weeding modalities from an actuation-oriented perspective. Specifically, we establish a comprehensive taxonomy of weeding approaches, including mechanical, chemical, thermal, laser-based, electrical, and other emerging methods, and analyze their underlying mechanisms and operational characteristics. Furthermore, we examine the coupling between sensing and actuation, highlighting how different intervention modalities impose distinct requirements on perception outputs. A scenario-based comparison framework is then developed to evaluate the suitability of different modalities across representative agricultural conditions, including pre-emergence control, in-row selective weeding, dense-row crop systems, and large weed situations. Based on this analysis, the limitations of single-modality systems are discussed, and emerging trends toward multi-modality integration and air–ground collaborative weed management are reviewed. Overall, this review shifts the focus from detection-centric approaches to the integration of sensing and actuation in robotic weeding systems and provides a decision-oriented framework to support the design, selection, and deployment of next-generation robotic weed management technologies. Full article

(This article belongs to the Special Issue Robotic Systems for Future Farming)

► Show Figures

Journal Menu

Journal Browser

Robotic Systems for Future Farming

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI