Early Detection of Drought and Overwatering Stress in Apple Seedlings Using Hyperspectral Imaging and Deep Learning

Abstract

Apple seedlings are of great economic importance, yet their susceptibility to water stress poses a serious threat to growth and yield, impacting horticultural productivity. Early detection can help mitigate losses, yet existing plant water stress detection methods are often invasive, time‐consuming, and difficult to apply to the sensitive seedling stage. This study pursued the goal of early, accurate, and non‐invasive identification of water stress (normal, drought, and overwatering) in apple seedling leaves by integrating several analytical techniques: endmember extraction, two‐dimensional correlation analysis, pseudo‐image analysis, and full‐channel hyperspectral imaging data. Using M9T337 apple seedlings as the research object, a total of 570 hyperspectral image data cubes were acquired for analysis and modeling. First, the N‐FINDR algorithm extracted pure spectral endmembers. Second, two‐dimensional correlation analysis revealed the temporal patterns of physiological indicators under water stress. Next, pseudo‐color images from RGB wavelengths were classified by an improved SimAM‐ECA‐MobileNet model and other transfer learning architectures. For the final step of processing whole‐channel hyperspectral data, we employed 1D versions of these same classifiers—specifically adapted for the task, including a 1D SimAM‐ECA‐MobileNet network. Our study proposes a dual‐attention deep learning model based on the MobileNet architecture for accurate classification of plant health status. The network combines the SimAM and ECA attention mechanisms, using a weighted fusion strategy to effectively improve the model's ability to identify plant drought, normal, and overwatered states. This method achieved a classification accuracy of 93.33% on the validation set of pseudoimages, significantly outperforming the baseline MobileNet model (81.67%). The proposed dual‐attention model establishes a robust and efficient framework for early water stress diagnosis in apple seedlings.