Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

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.

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

“…LiDAR sensors have been utilized in crop row detection to provide highly accurate and detailed 3D maps of crop canopies [47]. Additionally, LiDAR sensors have the capability to penetrate vegetation and capture ground surface data, facilitating the detection of crop rows, even in densely vegetated fields [22]. LiDAR can be used in intensive agricultural scenarios.…”

“…Agricultural robots can include modified tractors, small ground robots and aerial robots [13]. Modern agricultural equipment integrates advanced technologies, such as artificial intelligence, navigation, sensing systems and communication, to increase agricultural productivity and promote smart agriculture [22,122,123]. Among the information, navigation data, image recognition data, etc., re- Hile Narmilan Amarasingam et al [40] studied the potential of machine learning (ML) algorithms for the detection of mouse-ear grass leaves and flowers from multispectral (MS) images acquired by unmanned aerial vehicles (UAVs) at different spatial resolutions and compared different machine learning.…”

“…For example, Imran Zualkernan et al [21] focused on new deep learning models and architectures for research using drone image data since 2018. Jiayou Shi et al [22] presented a thorough review of the methods and applications related to crop row inspection in agricultural machinery navigation. They paid special attention to sensors and systems used for crop row detection in order to validate their sensing and detection capabilities and, thus, improve their sensing and inspection capabilities.…”

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

“…At present, traditional methods and deep-learning-based methods are the two main research directions in the field of crop row centerline recognition [8]. However, the traditional crop line detection scheme is vulnerable to various environmental factors such as light and shadow [9].…”

“…On the other hand, most of the current research on crop row detection only focuses on straight crop rows, with little research on curved crop rows. Curved crop rows usually have irregular shapes, uncertain dimensional variations, and crop rows may obscure and overlap each other, which affect the accuracy and fitness of object detection [8,24].…”