This AI-based method uses plant reflectance data to identify and differentiate diseases(s) in crops, allowing for early and efficient disease detection. Early, rapid, and accurate plant disease identification is essential for implementing management strategies in a timely manner to minimize pathogen spread. However, diagnosis based on visual symptoms can be inaccurate and subjective since different diseases may present similar symptoms. The visual manifestation of symptoms may also only occur in later disease stages. Additionally, abiotic factors such as drought, salinity damage, and nutrient deficiencies can also produce symptoms similar to those caused by pathogens. Case studies for citrus canker in orange trees and laurel wilt in avocado trees highlight these drawbacks.
Sugar Belle, a variety of citrus trees infected with citrus canker, do not outwardly present symptoms of infection in the early stages. The trees often appear healthy, with the bacterial growth taking a few months to develop and present. In the case of laurel wilt in avocado trees, early-stage symptoms of infection manifest as yellowish leaves, transforming into a brownish color with necrotic and curly areas in the late stages of infection. These manifestations of symptoms are similar to those associated with nutritional deficiencies in avocado trees. Lab analysis of plant samples for disease detection is time-consuming and labor-intensive, highlighting a need for accurate and early detection of plant disease. The ability to detect plant diseases and stress factors in the early stages, even before symptoms visually appear, can help growers select and implement effective management tactics.
Researchers at the University of Florida developed an AI-based method for extracting plant reflectance data and developing biomarker signatures for plant identification and early disease detection. The data analytic methodology rapidly analyzes hyperspectral imaging data collected by unmanned aerial vehicles (UAV) to identify issues at an earlier stage. It provides the opportunity for interventional management to reduce losses, improving efficiency and reducing costs associated with crop management.
AI-based data analytic methodology rapidly analyzes UAV-collected hyperspectral and multispectral imaging data for accurate and efficient detection and classification of disease states for various crop plants
This AI-based data analytic method rapidly analyzes UAV-collected hyperspectral and multispectral imaging data to identify plant diseases in the early stages of infection. The Karhunen-Loeve Expansion (KLE) of the spectral reflectance data is taken from healthy and diseased plants to identify a basis for a set of functions, representing the distribution of the reflected signal energy. Through multivariate KLE analysis, a frequency reconstruction converts information into a wave function, forming a unique biomarker. These spectral identification biomarkers enable the development of a database containing healthy plant signatures and characteristic signatures for various diseases, nutritional deficiencies, and abiotic stress. This allows for less invasive classification and disease diagnostic techniques, early-disease detection, and rapid remedial action.
