Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

Wang, Da; Su, Rui; Xiong, Yanjie; Wang, Yuwei; Wang, Weiwei

doi:10.3390/s22218430

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…Therefore, it is important to leave at least 1.0 cm of internodes both above and under the sugarcane bud when cutting seeds. Using this approach, there are enough resources left for the sugarcane buds to germinate [ 21 , 26 , 32 ]. The established requirements for cutting sugar cane are shown in Fig 1 .…”

Section: Methodsmentioning

confidence: 99%

“…The cutting forces between the sugarcane stalk and the circular saw knives were studied by Wang et al [ 26 ], where it was stated that, when the unit is working, the circular saw blade rotates at high speed. To facilitate the study of the force on which sugarcane is cut, assuming that the cross-section of the sugarcane stalk is a regular circle, the direction returned by the disc knife is the positive direction of the ξ axis, the vertically upward direction is the positive direction of the axis, ignoring the friction in the ξ direction and the Oξψ fixed coordinate system is established with the center O of the disc knife as the origin.…”

Section: Methodsmentioning

confidence: 99%

“…The mechanical equilibrium Eqs ( 1 and 2 ) are listed according to the force applied to the sugarcane [ 26 ]: where: F t is the tangential force of the circular saw knives on the cutting site, N; F n is the positive pressure exerted by the circular saw knives on the cutting site, N; G is the sugarcane gravity, N; F N 1 is the supporting force of side baffle on sugarcane, N; F N 2 is the supporting force of the ground against the sugarcane, N; and θ is the angle at which the blade cuts the sugarcane.…”

Section: Methodsmentioning

confidence: 99%

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A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

Yang,

Nasrat,

Badawy

et al. 2024

PLoS ONE

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Egypt is among the world’s largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

Yang,

Nasrat,

Badawy

et al. 2024

PLoS ONE

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“…To meet the seed cutting requirements, the design of the seed cutting device is outlined in Figure 3 ; Wang et al, 2022 , which includes the cane stem node target detection system and the cane stem node cutting system; the cane stem node target detection system is mainly composed of monocular camera, Industrial Personal Computer(IPC), switching power supply, etc. ; the cane stem node cutting system is mainly composed of self-tensioning conveying mechanism, reciprocating crank slider transfer mechanism and high-speed rotary cutting mechanism, which complete the processes of conveying and cutting of cane respectively.…”

Section: Related Workmentioning

confidence: 99%

Sugarcane stem node detection and localization for cutting using deep learning

Wang

et al. 2022

Front. Plant Sci.

Self Cite

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IntroductionIn order to promote sugarcane pre-cut seed good seed and good method planting technology, we combine the development of sugarcane pre-cut seed intelligent 0p99oposeed cutting machine to realize the accurate and fast identification and cutting of sugarcane stem nodes.MethodsIn this paper, we proposed an algorithm to improve YOLOv4-Tiny for sugarcane stem node recognition. Based on the original YOLOv4-Tiny network, the three maximum pooling layers of the original YOLOv4-tiny network were replaced with SPP (Spatial Pyramid Pooling) modules, which fuse the local and global features of the images and enhance the accurate localization ability of the network. And a 1×1 convolution module was added to each feature layer to reduce the parameters of the network and improve the prediction speed of the network.ResultsOn the sugarcane dataset, compared with the Faster-RCNN algorithm and YOLOv4 algorithm, the improved algorithm yielded an mean accuracy precision (MAP) of 99.11%, a detection accuracy of 97.07%, and a transmission frame per second (fps) of 30, which can quickly and accurately detect and identify sugarcane stem nodes.DiscussionIn this paper, the improved algorithm is deployed in the sugarcane stem node fast identification and dynamic cutting system to achieve accurate and fast sugarcane stem node identification and cutting in real time. It improves the seed cutting quality and cutting efficiency and reduces the labor intensity.

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“…Srivastava S et al [18] proposed a novel network framework based on VGG and Inception for identifying sugarcane diseases and pests. Da Wang et al [19] employed the YOLOv5 network model to identify sugarcane internodes and designed a pre-cutting system for sugarcane varieties based on visual recognition technology, enabling cutting of different types of sugarcane. The aforementioned studies indicate that deep learning network models can provide new insights for sugarcane-related agricultural work.…”

Section: Introductionmentioning

confidence: 99%

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

Dai,

Luo,

Shi

et al. 2024

Preprint

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The rapid and accurate identification of sugarcane internodes is of great significance for tasks such as field operations and precision management in the sugarcane industry, and it is also a fundamental task for the intelligence of the sugarcane industry. However, in complex field environments, traditional image processing techniques have low accuracy, efficiency, and are mainly limited to server-side processing. Meanwhile, the sugarcane industry requires a large amount of manual involvement, leading to high labor costs. In response to the aforementioned issues, this paper employed YOLOv5s as the original model algorithm, incorporated the K-means clustering algorithm, and added the CBAM attention module and VarifocalNet mechanism to the algorithm. The improved model is referred to as YOLOv5s-KCV. We implemented the YOLOv5s-KCV algorithm on Jetson TX2 edge computing devices with a well-configured runtime environment, completing the design and development of a real-time sugarcane internode identification system. Through ablation experiments, comparative experiments of various mainstream visual recognition network models, and performance experiments conducted in the field, the effectiveness of the proposed improvement method and the developed real-time sugarcane internode identification system were verified. The experimental results demonstrate that the improvement method of YOLOv5s-KCV is effective, with an algorithm recognition accuracy of 89.89%, a recall rate of 89.95%, and an mAP value of 92.16%, which respectively increased by 6.66%, 5.92%, and 7.44% compared to YOLOv5s. The system underwent performance testing in various weather conditions and at different times in the field, achieving a minimum recognition accuracy of sugarcane internodes of 93.5%. Therefore, the developed system in this paper can achieve real-time and accurate identification of sugarcane internodes in field environments, providing new insights for related work in sugarcane field industries.

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Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode

Cited by 8 publications

References 22 publications

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

Sugarcane stem node detection and localization for cutting using deep learning

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

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