arrow
Return

Efficient Maize Tassel-Detection Method using UAV based remote sensing

delete2021-08-01
delete22
PRE
AI
A
Ajay Kumar *
S
Sai Vikas Desai
V
Vineeth N Balasubramanian
P
P. Rajalakshmi
W
Wei Guo
B
B. Balaji Naik
B
Balram Marathi
U
Uday B. Desai
DOI:10.1016/j.rsase.2021.100549delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Regular monitoring is worthwhile to maintain a healthy crop. Historically, the manual observation was used to monitor crops, which is time-consuming and often costly. The recent boom in the development of Unmanned Aerial Vehicles (UAVs) has established a quick and easy way to monitor crops. UAVs can cover a wide area in a few minutes and obtain useful crop information with different sensors such as RGB, multispectral, hyperspectral cameras. Simultaneously, Convolutional Neural Networks (CNNs) have been effectively used for various vision-based agricultural monitoring activities, such as flower detection, fruit counting, and yield estimation. However, Convolutional Neural Network (CNN) requires a massive amount of labeled data for training, which is not always easy to obtain. Especially in agriculture, generating labeled datasets is time-consuming and exhaustive since interest objects are typically small in size and large in number. This paper proposes a novel method using k-means clustering with adaptive thresholding for detecting maize crop tassels to address these issues. The qualitative and quantitative analysis of the proposed method reveals that our method performs close to reference approaches and has an advantage over computational complexity. The proposed method detected and counted tassels with precision: 0.97438, recall: 0.88132, and F1 Score: 0.92412. In addition, using maize tassel detection from UAV images as the task in this paper, we propose a semi-automatic image annotation method to create labeled datasets of the maize crop easily. Based on the proposed method, the developed tool can be used in conjunction with a machine learning model to provide initial annotations for a given image, modified further by the user. Our tool's performance analysis reveals promising savings in annotation time, enabling the rapid production of maize crop labeled datasets.
Keywords:
Automatic annotation
Labeled data
UAV based Remote sensing
Tassel detection
Maize crop
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

R
Remote Sensing Applications Society and Environment
IF:
4.5
Papers:
1.4K
Citations:
5.3K

Organization

I
indian institute of technology system (iit system)
Scholars:
9.5W
Papers: 9.9W
Citations: 93
I
indian institute of technology (iit) - hyderabad
Scholars:
1.8K
Papers: 1.7K
Citations: 7
Cited Papers

Cited Papers

Targeting Audiences and Content for Forest Fire Information Programs
err1986-04-01
err0
PREAI
errEdwin H. Carpenter; Jonathan G. Taylor; Hanna J. Cortner; Philip D. Gardner; Malcolm J. Zwolinski; Terry C. Daniel
errShare
errSave
err
IF0
err
err0
PREAI
err
errShare
errSave
Importance of Algal Biomass to Growth and Development ofAnopheles gambiaeLarvae
err2006-07-01
err0
PREAI
errMichael G. Kaufman; Elizabeth Wanja; Shahnaz Maknojia; M. Nabie Bayoh; John M. Vulule; Edward D. Walker
errShare
errSave
Soybean yield prediction from UAV using multimodal data fusion and deep learning
err2020-02-01
err588
errOAAI
errMaimaitijiang, Maitiniyazi; Sagan, Vasit; Sidike, Paheding; Hartling, Sean; Esposito, Flavin; Fritschi, Felix B.
errShare
errSave
CAPACITIVE IMMUNOSENSOR FOR THE DETERMINATION OFSCHISTOSOMA JAPONICUMANTIGEN
err2002-09-24
err0
PREAI
errYa-Min Zhou; Shen-Qin Hu; Zhi-Xiang Cao; Guo-Li Shen; Ru-Qin Yu
errShare
errSave
Post-seismic monitoring of cliff mass wasting using an unmanned aerial vehicle and field data at Egremni, Lefkada Island, Greece
err2020-10-01
err36
PREAI
errKoukouvelas, Ioannis K.; Nikolakopoulos, Konstantinos G.; Zygouri, Vasiliki; Kyriou, Aggeliki
errShare
errSave
Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees
err2013-02-01
err240
PREAI
errGarcia-Ruiz, Francisco; Sankaran, Sindhuja; Maja, Joe Mari; Lee, Won Suk; Rasmussen, Jesper; Ehsani, Reza
errShare
errSave
Interactive effects of drought and heat stresses on morphophysiological attributes, yield, nutrient uptake and oxidative status in maize hybrids
err2019-03-07
err409
errOAAI
errHussain, Hafiz Athar; Men, Shengnan; Hussain, Saddam; Chen, Yinglong; Ali, Shafaqat; Zhang, Sai; Zhang, Kangping; Li, Yan; Xu, Qiwen; Liao, Changqing; Wang, Longchang
errShare
errSave
High- thick-film magnetic field sensor based on harmonic generation effects
err1999-01-01
err0
PREAI
errNeeraj Khare; A K Gupta; S K Shrivastava; Sangeeta Khare; V P N Padmanaban
errShare
errSave
researcher View more