UAV Based Remote Sensing for Tassel Detection and Growth Stage Estimation of Maize Crop Using Multispectral Images

Kumar, A. and Rajalakshmi, P. and Desai, U.B. and et al, . (2020) UAV Based Remote Sensing for Tassel Detection and Growth Stage Estimation of Maize Crop Using Multispectral Images. In: International Geoscience and Remote Sensing Symposium (IGARSS), 26 September 2020 - 2 October 2020.

Full text not available from this repository. (Request a copy)

Abstract

The monitoring of growth stages of a crop is vital for farmers to optimize the use of agronomic inputs and crop-management. Manual observation of the growth stages of a crop in large fields is a time consuming and labor-intensive task. To reduce human efforts in this tedious work, Unmanned Aerial Vehicle (UAV)-based remote sensing with the emergence of different technologies like deep learning is helping in monitoring the health of a crop. However, Convolutional Neural Network (CNN) based models need a lot of labeled data and computations to get trained to automate the process. In this paper, a pixel-based segmentation method has been proposed for tassel detection, and estimation of growth stages like tasseling, and day to 50% tasseling of maize crop. The performance analysis shows that the proposed method reduces time in developing the dataset for the training of CNN models. It also gives an advantage over training-time and computational complexity when compared to CNN models like YOLO and Faster-RCNN.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Rajalakshmi, PUNSPECIFIED
Desai, U BUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: CNN models; Crop managements; Growth stages; Labor intensive; Multispectral images; Over trainings; Performance analysis; Pixel-based segmentation
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 09 Jul 2021 09:20
Last Modified: 18 Feb 2022 06:35
URI: http://raiith.iith.ac.in/id/eprint/8194
Publisher URL: http://doi.org/10.1109/IGARSS39084.2020.9323266
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 8194 Statistics for this ePrint Item