Innovative Methods for Diagnosis Evergreen Utilizing Ultraspectral Image Inspection: A Review

Abstract

Plant diseases result in large financial losses in the global agricultural production industry. Regarding crop production, early disease detection, measurement, and identification are essential for the focused use of control measures. Extensive scientific research is now underway to provide novel hyperspectral technology-based solutions for plant disease diagnosis. By looking at the reflectance spectrum of plant tissue, you can tell the difference between healthy and sick plants, figure out how bad the disease is, name different pathogen species, and find early signs of biotic stress, even when the symptoms are not readily apparent to the unaided eye during the incubation phase. The review covers of fundamentals determining the reflectance spectrum of plant tissue. There is a discussion and evaluation of the potential applications of several kinds of hyperspectral Sensor technology and platforms for plant disease examination. Hyperspectral analysis, a relatively new field, employs techniques from image analysis and optical spectroscopy to measure physiological and morphological characteristics simultaneously. The following are the key phases of hyperspectral data analysis: modeling and data analysis; data extraction and processing; and picture collection and pre-processing. The algorithms and techniques used in each step are listed, then examine the primary uses of hyperspectral sensors in plant disease diagnosis, which include illness detection, disease classification and identification, damage assessment, andgenetic resistance evaluation. An extensive analysis of scholarly literature highlights the advantages of hyperspectral technology in investigating pathogen-plant interactions across various measurement scales. Despite significant advancements in plant disease monitoring using hyperspectral technology over the past few decades, several unresolved technical issues still hinder their practical application. Finally, we explore the issues and future directions for the application of new technology in agriculture.