ReviewiScience2024
Agricultural machinery automatic navigation technology.
Review in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Maize Detection and Row Extraction Using Maize-YOLO and IPM-Clustering Method for Autonomous Agricultural Navigation.Sensors (Basel, Switzerland) · 2026Article
- EMAF-Net: A Lightweight Single-Stage Detector for 13-Class Object Detection in Agricultural Rural Road Scenes.Sensors (Basel, Switzerland) · 2026Article
- UAV-Based Coverage Path Planning for Unmanned Agricultural Vehicles.Sensors (Basel, Switzerland) · 2026Article
- Mobile Ground-Truth 3D Detection Environment for Agricultural Robot Field Testing.Sensors (Basel, Switzerland) · 2025Article
- Construction and Enhancement of a Rural Road Instance Segmentation Dataset Based on an Improved StyleGAN2-ADA.Sensors (Basel, Switzerland) · 2025Article
- Technology-driven approaches to intelligent mechanical weed control: a systematic review for sustainable weed management.Frontiers in plant science · 2025Review
- LESA-Net: Semantic segmentation of multi-type road point clouds in complex agroforestry environment.Heliyon · 2024Article
- From compaction to coexistence: A review of chassis engineering for intelligent agricultural machinery in complex paddy field ecosystems.Science progressReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we review, compare, and analyze previous studies on agricultural machinery automatic navigation and path planning technologies. First, the paper introduces the fundamental components of agricultural machinery autonomous driving, including automatic navigation, path planning, control systems, and communication modules. Generally, the methods for automatic navigation technology can be divided into three categories: Global Navigation Satellite System (GNSS), Machine Vision, and Laser Radar. The structures, advantages, and disadvantages of different methods and the technical difficulties of current research are summarized and compared. At present, the more successful way is to use GNSS combined with machine vision to provide guarantee for agricultural machinery to avoid obstacles and generate the optimal path. Then the path planning methods are described, including four path planning algorithms based on graph search, sampling, optimization, and learning. This paper proposes 22 available algorithms according to different application scenarios and summarizes the challenges and difficulties that have not been completely solved in the current research. Finally, some suggestions on the difficulties arising in these studies are proposed for further research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.