Detection Of Crop Water Stress In Maize Using Drone Based Hyperspectral Imaging

Abstract

Crop water stress is one of the major factors limiting crop productivity. Maize (Corn) crop is very sensitive to water stress. Efficient monitoring and detection of water stress is crucial for precision irrigation and sustainable agriculture. The main objective of this study is to detect water stress during grain-fill stage in maize crops using hyperspectral (HS) observations. Maize hybrid field trial plots were selected from Hyderabad region, Telangana, India. HS images of the study area were collected using a hexacopter drone mounted with line scanning HS camera having spectral range of 450–950 nanometer (nm). The line scanning HS drone observations were processed for georeferencing, mosaicing, and ortho-rectification. The study area was divided into two sub-plots, viz. control (i.e., no stress) and water stress during grain-fill stage of the crop. Each plot had 66 sub-plots, each representing a unique variety. Visual spectral analysis was performed to identify the differences and spectral regions suitable for water stress detection. Analysis revealed that the visible region around 680 nm and the Red-Edge (RE) - Near-InfraRed (NIR) region were providing the differences between the no-stress and water stressed plots. The optimal subset of wavelength regions were identified using machine learning (ML) based feature selection. Random Forest (RF) and Support Vector Machine (SVM) classifiers were used to assess the feasibility of the chosen bands. SVM with top 10 bands in the range of 670–780 nm found most effective for detecting grain-fill water stress in maize crop varieties.