Vegetation detection based on spectral information and development of a low-cost vegetation sensor for selective spraying

被引:5
作者
Wang, Aichen [1 ]
Li, Wei [1 ]
Men, Xiaohai [1 ]
Gao, Binjie [1 ]
Xu, Yifei [2 ,3 ]
Wei, Xinhua [1 ]
机构
[1] Jiangsu Univ, Key Lab Modern Agr Equipment & Technol, Minist Educ, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Nanchang Huiyichen Technol Ltd, Nanchang, Jiangxi, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
vegetation detection; spectroscopy; sensor; selective spraying; precision agriculture; RANDOM FOREST; SPECTROSCOPY; EXTRACTION;
D O I
10.1002/ps.6874
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
BACKGROUND Real-time site-specific selective spraying for crop protection against weeds, pests and diseases requires fast and precise detection of targets. This study investigated several aspects of vegetation target detection based on spectral information and developed a low-cost vegetation sensor. Specific objectives included: (1) compare vegetation-detection performance using reflected and fluorescence data; (2) evaluate the effect of light source and bandwidth, and select the optimal light source and waveband; and (3) develop a low-cost vegetation sensor and test its performance under different light conditions. RESULTS The main outcomes of this study are as follows. (1) The fluorescence excited by blue and red light-emitting diodes (LEDs) was an effective attribute with which to differentiate vegetation from non-vegetation and provided results comparative with reflected spectroscopy. (2) A blue LED could excite strong fluorescence and was recommended as the light source. (3) Under illumination by blue and red LEDs, bandwidth did not have any obvious effect on classification accuracy within the studied bandwidths from 0 nm (single wavelength) to 120 nm. (4) A low-cost vegetation sensor was developed and tested, with 100% detection accuracy in both dark and outdoor environments. CONCLUSION This study suggests that single-waveband fluorescence spectroscopy is an effective approach to detect vegetation targets and low-cost LEDs can be used for illumination. Light source modulation with a sinusoidal signal is an effective way to resist the influence of environmental light. Overall, the findings of this study contribute to an improved understanding of developing low-cost and effective vegetation sensors for selective spraying from theory to application. (c) 2022 Society of Chemical Industry.
引用
收藏
页码:2467 / 2476
页数:10
相关论文
共 28 条
  • [1] Site-specific weed management: sensing requirements - what do we need to see?
    Brown, RB
    Noble, SD
    [J]. WEED SCIENCE, 2005, 53 (02) : 252 - 258
  • [2] Cristianini N., 2000, An introduction to support vector machines and other kernel-based learning methods
  • [3] Spectral differentiation of sugarcane from weeds
    de Souza, Micael Felipe
    do Amaral, Lucas Rios
    de Medeiros Oliveira, Stanley Robson
    Neris Coutinho, Marcos Antonio
    Netto, Camila Ferreira
    [J]. BIOSYSTEMS ENGINEERING, 2020, 190 : 41 - 46
  • [4] Study on Spectral Detection of Green Plant Target
    Deng Wei
    Zhao Chun-jiang
    He Xiong-kui
    Chen Li-ping
    Zhang Lu-da
    Wu Guang-wei
    Mueller, J.
    Zhai Chang-yuan
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30 (08) : 2179 - 2183
  • [5] Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation
    Devos, Olivier
    Ruckebusch, Cyril
    Durand, Alexandra
    Duponchel, Ludovic
    Huvenne, Jean-Pierre
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2009, 96 (01) : 27 - 33
  • [6] Dhanoa M., 1994, J NEAR INFRARED SPEC, V2, P43, DOI [10.1255/jnirs.30, DOI 10.1255/JNIRS.30]
  • [7] Random forest and leaf multispectral reflectance data to differentiate three soybean varieties from two pigweeds
    Fletcher, Reginald S.
    Reddy, Krishna N.
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2016, 128 : 199 - 206
  • [8] Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields
    Gao, Junfeng
    French, Andrew P.
    Pound, Michael P.
    He, Yong
    Pridmore, Tony P.
    Pieters, Jan G.
    [J]. PLANT METHODS, 2020, 16 (01)
  • [9] Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery
    Gao, Junfeng
    Nuyttens, David
    Lootens, Peter
    He, Yong
    Pieters, Jan G.
    [J]. BIOSYSTEMS ENGINEERING, 2018, 170 : 39 - 50
  • [10] Goutte C, 2005, LECT NOTES COMPUT SC, V3408, P345