Detection of Curved Rows and Gaps in Aerial Images of Sugarcane Field Using Image Processing Techniques

Rocha, Bruno Moraes; Vieira, Gabriel da Silva; Fonseca, Afonso U.; Sousa, Naiane Maria de; Pedrini, Hélio; Soares, Fabrízzio

doi:10.1109/icjece.2022.3178749

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Curved crop rows usually have complex shapes and occur not only in agricultural terraces but also in flat plots with irregular geometry. This makes it difficult to accurately detect and measure target crop rows, and the traditional line detection algorithm is difficult to cope with in this situation as it can bring certain challenges for the safe navigation of agricultural robots [7]. Therefore, in order to improve the accuracy of crop row detection, appropriate sensors, and computer algorithms need to be selected according to different application scenarios [8].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Rethinking the crop row detection pipeline: An end-to-end method for crop row detection based on row-column attention

Li,

Wei

et al. 2024

Computers and Electronics in Agriculture

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Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ

Yano,

de Lima,

Speranza

et al. 2024

Applied Sciences

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Sugarcane plays a pivotal role in the Brazilian economy as a primary crop. This semi-perennial crop allows for multiple harvests throughout its life cycle. Given its longevity, farmers need to be mindful of avoiding gaps in sugarcane fields, as these interruptions in planting lines negatively impact overall crop productivity over the years. Recognizing and mapping planting failures becomes essential for replanting operations and productivity estimation. Due to the scale of sugarcane cultivation, manual identification and mapping prove impractical. Consequently, solutions utilizing drone imagery and computer vision have been developed to cover extensive areas, showing satisfactory effectiveness in identifying gaps. However, recognizing small gaps poses significant challenges, often rendering them unidentifiable. This study addresses this issue by identifying and mapping gaps of any size while allowing users to determine the gap size. Preliminary tests using YOLOv5 and ImageJ 1.53k demonstrated a high success rate, with a 96.1% accuracy in identifying gaps of 50 cm or larger. These results are favorable, especially when compared to previously published works.

show abstract

Detection of Curved Rows and Gaps in Aerial Images of Sugarcane Field Using Image Processing Techniques

Cited by 3 publications

References 33 publications

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

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

Rethinking the crop row detection pipeline: An end-to-end method for crop row detection based on row-column attention

Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ

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