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

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

Optimal Parameter Selection for UAV Based Pushbroom Hyperspectral Imaging

Hyperspectral imaging (HSI) sensors acquire rich spectral information of objects in hundreds of narrow spectral bands, which can be useful in extracting unique features. In recent years, Unmanned Aerial Vehicle (UAV) based HSI techniques are widely used in remote sensing fields due to their wide field of coverages, short revisiting periods, high spectral and spatial resolutions. Pushbroom sensors are line scanners, which acquire data in lines/frames. When a push-broom HSI sensor is used in a UAV platform, the image quality, ground pixel resolution are governed by the UAV and sensor operating parameters, which need to be carefully chosen. In this paper, we propose a mathematical approach for choosing the optimal combination of operating parameters such as UAV speed, flight altitude, sensor frame rate to acquire quality hyperspectral (HS) images with desired ground pixel resolution. Different combinations of camera and flight parameters were tested and evaluated with the classification performance of a convolutional neural network (CNN) model on acquired different vegetation HSI data. We obtained classification accuracies of 96.78%, 97.65%, and 95.55% on HS images acquired from 30m, 40m, and 50m flight altitudes respectively. © 2021 IEEE

Efficient Processing Methodology for UAV Flight Path Detection

Unmanned Areal Vehicle (UAV) based imagery is an emerging technology that has penetrated numerous verticals such as remote sensing, precision agriculture, land surveying. Various types of sensors are mounted onto UAV, and the images of the area of interest are captured. To get a complete distortionless areal view of the area, an orthomosaic is created using the captured images on which further analysis is done. But the traditional orthomosaic creation techniques are tedious, time-consuming, and also computationally complex. In this paper, a novel algorithm is proposed which speeds up the region of interest (ROI) detection significantly. In this method, the UAV flight path is divided into multiple Sub-Paths, and each path is processed parallelly. This method is universal and drastically improves the processing speed for any set of UAV images. It is observed that the algorithm reduces the computation time by around 75%