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

被引:7
|
作者
Wang, Da [1 ]
Su, Rui [1 ]
Xiong, Yanjie [1 ]
Wang, Yuwei [1 ,2 ]
Wang, Weiwei [1 ,2 ]
机构
[1] Anhui Agr Univ, Sch Engn, Hefei 230036, Peoples R China
[2] Anhui Prov Engn Lab Intelligent Agr Machinery & E, Hefei 230036, Peoples R China
关键词
sugarcane; computer vision; precision agriculture; pre-cutting mode; YOLO V5;
D O I
10.3390/s22218430
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
China is the world's third-largest producer of sugarcane, slightly behind Brazil and India. As an important cash crop in China, sugarcane has always been the main source of sugar, the basic strategic material. The planting method of sugarcane used in China is mainly the pre-cutting planting mode. However, there are many problems with this technology, which has a great impact on the planting quality of sugarcane. Aiming at a series of problems, such as low cutting efficiency and poor quality in the pre-cutting planting mode of sugarcane, a sugarcane-seed-cutting device was proposed, and a sugarcane-seed-cutting system based on automatic identification technology was designed. The system consists of a sugarcane-cutting platform, a seed-cutting device, a visual inspection system, and a control system. Among them, the visual inspection system adopts the YOLO V5 network model to identify and detect the eustipes of sugarcane, and the seed-cutting device is composed of a self-tensioning conveying mechanism, a reciprocating crank slider transmission mechanism, and a high-speed rotary cutting mechanism so that the cutting device can complete the cutting of sugarcane seeds of different diameters. The test shows that the recognition rate of sugarcane seed cutting is no less than 94.3%, the accuracy rate is between 94.3% and 100%, and the average accuracy is 98.2%. The bud injury rate is no higher than 3.8%, while the average cutting time of a single seed is about 0.7 s, which proves that the cutting system has a high cutting rate, recognition rate, and low injury rate. The findings of this paper have important application values for promoting the development of sugarcane pre-cutting planting mode and sugarcane planting technology.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] How governments seek to bridge the financing gap for university spin-offs: proof-of-concept, pre-seed, and seed funding
    Rasmussen, Einar
    Sorheim, Roger
    TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT, 2012, 24 (07) : 663 - 678
  • [32] Evaluation of 9,10 anthraquinone application to pre-seed set sunflowers for repelling blackbirds
    Niner, Megan D.
    Linz, George M.
    Clark, Mark E.
    HUMAN-WILDLIFE INTERACTIONS, 2015, 9 (01): : 4 - 13
  • [33] Machine Vision-based Selection Machine of Corn Seed Used for Directional Seeding
    Wang Q.
    Chen B.
    Zhu D.
    Liangxi H.
    Dai H.
    Chen H.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2017, 48 (02): : 27 - 37
  • [34] Pre-seed workshop: Success in launching high-tech start-up companies
    Albers, Judith J.
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2012, 244
  • [35] STUDIES ON THE MODE OF INFECTION AND SPREAD OF PHYTOPHTHORA IN SUGARCANE SEED PIECES
    VANDERZWET, T
    STEIB, RJ
    PHYTOPATHOLOGY, 1959, 49 (09) : 553 - 553
  • [36] EFFECT OF PRE-SEED PRE-EMERGENCE APPLICATION OF NITRALIN, TRIFLURALIN AND THEIR COMBINATIONS WITH FLUOMETURON ON COTTON YIELD AND ASSOCIATED WEEDS
    ELDEBABY, AS
    RIZK, TY
    SHAFSHAK, SED
    CAIRO, GAS
    BEITRAGE ZUR TROPISCHEN LANDWIRTSCHAFT UND VETERINARMEDIZIN, 1978, 16 (02): : 173 - 178
  • [37] Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning
    Xu, Peng
    Tan, Qian
    Zhang, Yunpeng
    Zha, Xiantao
    Yang, Songmei
    Yang, Ranbing
    AGRICULTURE-BASEL, 2022, 12 (02):
  • [38] MACHINE VISION IDENTIFICATION OF DIPLOID AND TETRAPLOID RYEGRASS SEED
    BERLAGE, AG
    COOPER, TM
    ARISTAZABAL, JF
    TRANSACTIONS OF THE ASAE, 1988, 31 (01): : 24 - 27
  • [39] A novel method for seed cotton color measurement based on machine vision technology
    Li, Hao
    Zhang, Ruoyu
    Zhou, Wanhuai
    Liu, Xiang
    Wang, Kai
    Zhang, Mengyun
    Li, Qingxu
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2023, 215
  • [40] Research Progress of Machine Vision in Crop Seed Inspection
    Wang, Hao
    Zhu, Yuhua
    Li, Zhihui
    Zhen, Tong
    Computer Engineering and Applications, 2023, 59 (22) : 69 - 83