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

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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

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30 pages, 25723 KB

Open AccessArticle

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 321

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 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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