Rubber Tapping Position and Harvesting Cup Detection Using Faster-RCNN with MobileNetV2

Wongtanawijit, Rattachai; Khaorapapong, Thanate

doi:10.1109/icsec47112.2019.8974731

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…To date, many technologies, such as sensors, machine vision, Lidar, and ultrasonic geomagnetic position [83,84], have been developed to study the autonomous navigation of robots [85]. Some researchers have aimed to develop an algorithm of a vision system for the replacement of the expensive sensors used in typical autonomous vehicles [86][87][88]. The vision system model was implemented using only one camera installed on the vehicle to search for calibrated targets that were put on the trunks of rubber trees.…”

Section: Obstacle Information Perception and Path Planningmentioning

confidence: 99%

“…This feature can be combined with other features such as texture features to enhance the performance of the model in classifying this type of data. Some researchers [87] proposed a tapping-path and latex cup detection algorithm using a Faster-RCNN detector, which is a state-of-the-art precision detector. Their acquisition tool integrated an RGB-D camera with assisting lights for the capturing of images under low-light conditions.…”

Section: Recognition Of Natural Rubber Trees and Tapping Linesmentioning

confidence: 99%

“…Protective research of rubber trees 2018 S29 [87] This paper presented the detection of rubber tree (Hevea brasiliensis) tapping positions (tapping-paths) and trunk-mounted harvesting cups in RGB-D images, representing the machine vision part of an automatic rubber tapping system.…”

Section: Paper Id Information Extraction and Future Work Category Yearmentioning

confidence: 99%

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The Development of Rubber Tapping Machines in Intelligent Agriculture: A Review

et al. 2022

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In the past decade, intelligent technologies have advanced rapidly, particularly due to improvements in automatic control, which have had a significant impact on forestry, as well as animal husbandry and the future of farm management. However, the degree of production and management mechanization in natural rubber plantations is low, especially since the methods of tapping still rely heavily on labor. The decrease of skilled rubber tappers and the increase in labor costs have led to the development of the mechanization of rubber tapping operations. The application of emerging intelligent agricultural technologies could provide an alternative in order to maximize the potential productivity of natural rubber. Based on this vision, we reviewed the literature on rubber tapping from the past decade for system implementation in rubber plantations. In this review, selected references on rubber tapping were categorized into several directions of research, including rubber tapping machines, the key technologies applied in tapping operations, and some related protective research, analyzing research works from 2010 to 2022 that focused on tapping methods. The review also discusses the application of intelligent agricultural technologies, such as the recognition of tapping trajectory and tapping path planning. A summary of challenges and future trends is also provided in this study. Based on the relevant research, the use of intelligent technologies in rubber tapping machines is still in its initial stage and has broad prospects. Through this study, we aim to provide a reference for researchers in the field of rubber tapping machines and thus to play a positive role in future rubber tapping.

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