A Comprehensive Review of Plant Root-Stem Detection Methods
Year: 2026
Type: article
Abstract: The ground-surface portions of plant roots (GSPR) and stems play a crucial role in precision agriculture. Accurate detection of the GSPR enables precise target localization for intelligent irrigation systems, thereby facilitating precise irrigation. Furthermore, reliable detection of both the ground-surface portions of plant roots and stems provides essential support for a wide range of agricultural applications, including intelligent weeding, agricultural robot navigation, crop growth monitoring, and plant phenotyping. However, agricultural environments are characterized by significant illumination variations, complex backgrounds, severe occlusions, and the high morphological similarity between roots and soils, all of which make robust detection particularly challenging. In addition, publicly available datasets for the detection of GSPR and stems remain limited. Consequently, research on object detection of the GSPR and stems is still in its early stages. A comprehensive review of detection techniques for the GSPR and stems is still lacking. This paper presents a systematic review of recent advances in the detection of ground-surface portions of plant roots GSPR and stems. It covers GSPR and stems datasets, the application of sensing technologies, the development of detection algorithms, and existing practical application scenarios. In addition, this review evaluates dataset complexity and algorithmic robustness, and discusses the current challenges and future research directions. Overall, this review aims to systematically summarize recent advances in GSPR and stem detection, analyze mainstream sensing technologies and detection algorithms, and provide a comprehensive reference for future research and practical applications in precision agriculture.
Source: Sensors
DOI: 10.3390/s26227101
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225708143