Affiliation:
1. College of Engineering, Nanjing Agricultural University, Nanjing 210095, China
2. School of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
3. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Abstract
Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.
Funder
Jiangsu Modern Agricultural Equipment and Technology Demonstration & Promotion Project
National Natural Science Foundation of China
Jiangsu Agricultural Science and Technology Innovation Fund
Subject
Agronomy and Crop Science
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