Hyperspectral imaging VIS-NIR and SWIR fusion for improved drought-stress identification of strawberry plants

Hyperspectral imaging systems that operate in the visible-near infrared (VIS-NIR) and short-wave infrared (SWIR) spectral regions are increasingly recognized as practical and effective tools for enhancing crop management. However, hyperspectral systems can have some limitations when focusing on specific spectral ranges, particularly for spatial and spectral resolution. Image fusion techniques combining information from different sensors to enhance hyperspectral data can significantly improve spatial and spectral resolution. Fusion data of image and spectral data from the two HSI cameras (VIS-NIR and SWIR) provide complementary information on plant physiology, biochemistry, and morphology before visible plant stress symptoms. This study presents advancements in hyperspectral image fusion achieved by using two line-scan sensors, one for VIS-NIR (397–1003 nm) and the other for SWIR (894–2504 nm), to detect asymptomatic drought stress in strawberry plants. The images from both hyperspectral imaging systems were aligned based on feature and intensity, combined with various geometric transformations for fusion. The resulting fused hyperspectral cube contained 403 bands covering a broad spectrum from 397 to 2500 nm. Given the vulnerability of strawberry plants to drought, which can significantly affect growth and yield, this study aimed to explore the potential of hyperspectral image fusion for high-throughput detection of drought-stressed strawberry plants. The fused images improved the performance of the PLS-DA detection model, increasing classification accuracy by up to 10 %, achieving 99 % accuracy in the prediction set, and reducing error rates compared to independently generated models.