Hyperspectral imaging using deep learning in wheat diseases: (Review)

Wheat is a significant staple crop worldwide, but its cultivation is challenged by various factors such as pests, diseases, climate change, lack of water, and yield enhancement. To prevent wheat infections, nondestructive and self-sufficient remote detection methods are necessary, which are sensitive and reliable. Hyperspectral imaging with deep learning is a powerful method for identifying diseases early, mapping them accurately, and evaluating their severity in plants in an unbiased way. This method involves data collection, preprocessing, and analysis of Hyperspectral images to identify wheat diseases. The article also presents several Hyperspectral imaging datasets and relevant research on using this technology for detecting wheat illnesses. The integration of spectroscopy and imaging in Hyperspectral imaging represents a novel approach to achieving an in-depth and comprehensive understanding of plant life. Moreover, deep learning, a subset of artificial intelligence, has gained considerable attention due to its ability to process vast and intricate data sets. By synergistically merging these two advanced technologies, researchers and scientists can significantly enhance the analysis of hyperspectral imagery, thereby enabling improved crop monitoring, disease identification, and yield projection in wheat farming This article summarizes the use of deep learning techniques in Hyperspectral imaging for wheat crops. Hyperspectral imaging is a non-destructive way to study crops but generates a lot of data that can be difficult to analyze. Deep learning automates the analysis process and improves accuracy. However, it requires large and diverse datasets, complex model development, and computational resources. This report provides information on the benefits and challenges of using deep learning in Hyperspectral imaging for wheat crops.