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From Innovation to Field Adoption: Sensing and Robotic Systems in Smart Agriculture

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

Deadline for manuscript submissions: 15 January 2027 | Viewed by 1031

Special Issue Editors

Dr. Thomas A. Ciarfuglia

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Guest Editor
San Raffaele University of Rome, 00166 Rome, Italy
Interests: artificial intelligence and robotics; smart agriculture; hyperspectral sensing; pattern recognition; robot perception; SLAM
Dr. Francesco Leotta

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Guest Editor
Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Interests: smart manufacturing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giorgio Grisetti

E-Mail Website
Guest Editor
Department of Systems and Computer Science, Sapienza University of Rome, 00185 Rome, Italy
Interests: mobile robotics; SLAM
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart Agriculture, or Precision Farming, aims to integrate advanced technologies into agricultural processes that are already highly refined and optimized. A significant challenge slowing the adoption of new technologies is that novel sensors and robotic systems must prove their value and field-effectiveness without disrupting this optimization. Agriculture requires cutting-edge, field-proven technologies that respect the holistic process of food production.

This Special Issue invites contributions on novel sensing systems, robotic applications, and data-driven technologies that directly address this challenge. We aim to collect works that bridge the gap between technological innovation and the practical, economic, and operational demands of the agricultural industry.

We strongly encourage submissions covering new sensor technologies, data-driven and AI-based processing (including edge devices), sensor fusion, autonomous navigation, and robotic systems that demonstrate a clear focus on real-world effectiveness, scalability, and adoptability.

Topics for this issue could relate to, but are not limited to, the following:

  • Agricultural Robotics
  • Intelligent Sensing Systems
  • Robotic Perception
  • Multi-Sensor Fusion
  • Autonomous Agricultural Vehicles
  • AI for Precision Agriculture
  • Edge Computing for Agriculture
  • Agricultural Remote Sensing
  • Smart Soil and Crop Monitoring
  • Agricultural Technology Adoption

Dr. Thomas A. Ciarfuglia
Dr. Francesco Leotta
Prof. Dr. Giorgio Grisetti
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

  • agricultural robotics
  • intelligent sensing systems
  • robotic perception
  • multi-sensor fusion
  • autonomous agricultural vehicles
  • AI for precision agriculture
  • edge computing for agriculture
  • agricultural remote sensing
  • smart soil and crop monitoring
  • agricultural technology adoption

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

Published Papers (2 papers)

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Research

38 pages, 46338 KB  
Article
A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture
by Rong Zhao, Fei Deng, Haohua Que, Mingkai Liu, Xiejia Yue and Lei Mu
Sensors 2026, 26(11), 3474; https://doi.org/10.3390/s26113474 - 31 May 2026
Abstract
Tomato leaf diseases substantially reduce tomato yields and quality and remain a persistent challenge for efficient crop management. Although deep learning-based detectors have achieved strong accuracy in controlled benchmarks, many existing solutions are still difficult to transfer to resource-constrained agricultural systems because they [...] Read more.
Tomato leaf diseases substantially reduce tomato yields and quality and remain a persistent challenge for efficient crop management. Although deep learning-based detectors have achieved strong accuracy in controlled benchmarks, many existing solutions are still difficult to transfer to resource-constrained agricultural systems because they rely on high-end GPUs, consume considerable power, and often lose performance after deployment on embedded devices. To address this practical gap, this study proposes HGS-YOLO, a system-oriented deployable lightweight adaptation of YOLOv11 for leaf-level tomato disease detection, together with an end-to-end edge sensing pipeline for low-power agricultural deployment. The main contribution lies in the coordinated system-level co-design of model structure, optimization, and deployment rather than in a novel detector architecture. Specifically, YOLOv11 is adapted through three coordinated modifications: an HGNetV2 backbone for efficient feature extraction, an HS-FPN neck with channel attention for lightweight multi-scale fusion, and an MPDIoU loss function for more stable localization optimization. Beyond the model architecture, the study establishes a complete engineering pipeline that includes training, optimization, post-training quantization, and hardware deployment with BPU acceleration on a D-Robotics RDK X5 handheld platform. Comprehensive benchmark experiments indicate that HGS-YOLO achieves 93.6% mAP50 and 72.1% mAP@[0.5:0.95] with 86.5% recall, only 1.3 M parameters, and a 3.1 MB model size, substantially reducing the model complexity and storage cost relative to the YOLOv11 baseline. A three-seed retraining comparison shows that HGS-YOLO trades roughly 0.5 mAP50 points for this compactness (a statistically significant but small concession) and recovers the cost on the deployment side: on the RDK X5 chip, HGS-YOLO is the fastest, most memory-efficient, and lowest-power model among all compared detectors. Indoor deployment tests using separately collected tomato leaf samples further achieve 90.3% mAP50, 82.3% recall, 89.0% precision, 25.0 ± 0.4 ms end-to-end latency, 40.0 ± 0.6 FPS, and 9.8 ± 0.4 W average system power. After PTQ, the mAP50 drops from 93.6% to 93.0% on the same benchmark; because this figure was measured under controlled imaging conditions, it is presented as an in-distribution reference point rather than as evidence of robustness in the open field. We also took the handheld system into a working tomato greenhouse for a small outdoor field round, where it ran end-to-end and produced on-device disease detections under natural sunlight, specular highlights, partial occlusion, background clutter, and handheld motion blur. These results show that HGS-YOLO reaches a good balance of accuracy, efficiency, and deployability and that it works in the field on an independent small-scale test; validating it more widely across sites, seasons, and weather is left to future work. Full article
(This article belongs to the Special Issue From Innovation to Field Adoption: Sensing and Robotic Systems in Smart Agriculture)
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30 pages, 25723 KB  
Article
Maize Detection and Row Extraction Using Maize–YOLO and IPM–Clustering Method for Autonomous Agricultural Navigation
by Tao Sun, Junzhe Qu, Chen Cai, Yongkui Jin, Songchao Zhang, Feixiang Le, Xinyu Xue and Longfei Cui
Sensors 2026, 26(10), 2952; https://doi.org/10.3390/s26102952 - 8 May 2026
Viewed by 363
Abstract
Real-time and accurate crop row extraction is a fundamental requirement for vision-based perception in autonomous agricultural machinery. In maize fields, however, row detection is easily affected by variable illumination, leaf occlusion, weed interference, and uneven soil backgrounds, which can reduce the reliability of [...] Read more.
Real-time and accurate crop row extraction is a fundamental requirement for vision-based perception in autonomous agricultural machinery. In maize fields, however, row detection is easily affected by variable illumination, leaf occlusion, weed interference, and uneven soil backgrounds, which can reduce the reliability of both GNSS- and image-based navigation methods. To address these challenges, this study proposes a plant-oriented crop row perception framework that reconstructs row structures from individual maize plant detections. A lightweight detection model, named Maize–YOLO, was developed based on YOLOv11n for maize seedling detection. Three key improvements were introduced to enhance the balance between accuracy and efficiency. First, the C3k2_Faster_CGLU module replaces the original C3k2 block to reduce redundant convolutional computation while improving selective feature representation through convolutional gated linear units, thereby enhancing robustness under complex field backgrounds. Second, a lightweight shared detection head, Detect_LSH, was designed to share convolutional parameters across multi-scale feature maps and adaptively adjust feature amplitudes, reducing detection-head redundancy while maintaining multi-scale prediction capability. Third, a Layer-Adaptive Magnitude-Based Pruning strategy was applied to remove low-contribution channels and further improve computational efficiency for CPU-based deployment. Experimental results on field-collected maize seedling images showed that Maize–YOLO achieved an mAP@0.5 of 97.6%, reduced GFLOPs by 61.9%, and maintained a CPU inference speed of 84.4 FPS. After plant detection, row centerlines were estimated using an IPM–DBSCAN–LSM pipeline, which transformed detected plant centers into a quasi-top-view space, clustered them into crop rows, and fitted continuous centerlines. The extracted crop rows reached a positional accuracy of 98.6%, with a mean angular deviation of 0.44°. These results demonstrate that the proposed method can provide accurate, lightweight, and real-time crop row perception for autonomous agricultural navigation and precision field operations. Full article
(This article belongs to the Special Issue From Innovation to Field Adoption: Sensing and Robotic Systems in Smart Agriculture)
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