CNN Based Water Stress Detection in Chickpea Using UAV Based Hyperspectral Imaging

Sankararao, Adduru U G and Priyanka, Gattu and Rajalakshmi, P. and et al, . (2021) CNN Based Water Stress Detection in Chickpea Using UAV Based Hyperspectral Imaging. In: 2021 IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2021, 6 December 2021 through 10 December 2021, Virtual, Online.

[img] Text
2021_IEEE_India_Geoscience_and_Remote_Sensing_Symposium_InGARSS 2021_Proceedings1.pdf - Published Version
Restricted to Registered users only

Download (3MB) | Request a copy


Water is an important agronomic input, which plays a vital role in the health and yield of the crop. Water deficiency results in abiotic stress, early detection of water stress help in recovering the health of the crop. Hyperspectral imaging (HSI) sensors acquire rich spectral information of the objects in hundreds of narrow bands, are capable of identifying the change in canopy water content, which is crucial in predicting irrigation requirements of the crop. Due to the wide field of coverages, short revisiting periods, and high spectral resolutions, Unmanned Aerial Vehicle (UAV) based HSI techniques are suitable in precision agriculture. In this paper, water stress detection in chickpea canopy is presented using hyperspectral (HS) images acquired from UAV. The drought classification was performed in two ways, i. by considering selected water-sensitive bands, and ii. by considering the whole spectral bands of the HS images. A 3D-2D convolutional neural network (CNN) model is used to classify well-watered canopy from water-stressed one, and its performance is compared with that of a Support Vector Machine (SVM) and a 2D+1D CNN model in identifying water stress. We obtained the best classification accuracy of 95.44%, which shows the potential of HSI in successfully detecting water stress in chickpea. © 2021 IEEE.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Rajalakshmi, P
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Convolutional neural network; Deep learning; Hyperspectral imaging; Unmanned aerial vehicle; Water stress classification
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 13 Jul 2022 10:48
Last Modified: 13 Jul 2022 10:48
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 9664 Statistics for this ePrint Item