Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado

Abstract

Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish Lw infected avocado from healthy trees and trees with other abiotic stressors is presented in this paper. A novel method was developed to analyze data from hyperspectral data using finite difference approximation (FDA) and bivariate correlation (BC) to discriminate Lw, Nitrogen (N), and Iron (Fe) deficiencies from healthy avocado plants. Several combinatorial methods were used in preprocessing the data, such as standard normal transformation of data, smoothing of the data, and polynomial fit. The FDA technique was derived using a Taylor Polynomial finite difference approximation. This FDA accentuates inflection points in the spectrum. These, in turn, reveal variance in the data that can be used to identify spectral signature associated with healthy and diseased states. By statistical correlation using the bivariate correlation coefficient of these enhanced spectral patterns, an algorithm (FDA-BC) for distinguishing Lw avocado leaves from all other categories of healthy or mineral deficient avocado leaves is achieved with an overall accuracy of 100%.

Types of Unmanned Aerial Vehicles (UAVs), Sensing Technologies, and Software for Agricultural Applications

“…Researchers have been using hyperspectral sensors combined with machine learning algorithms to correlate the collected reflectance data with various agricultural parameters. For example, hyperspectral sensors are being used to detect, identify, and distinguish plant diseases with similar visual symptoms, which can be a very complex task (Abdulridha et al 2020a;Hariharan et al 2019).…”

Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning

“…One of the benefits of aerial imaging using UAVs is providing information on disease hot spots. Remote sensing (e.g., UAV-based hyperspectral imagery) can detect plants with diseases in asymptomatic and early disease development stages, which are critical for timely disease management ( Hariharan et al, 2019 ). Abdulridha et al (2019 , 2020a) successfully detected different disease development stages of laurel wilt in avocado and bacterial and target spots in tomatoes with high classification accuracies utilizing remote sensing and machine learning.…”

Abnormal Operation Tracking through Big-Data-Based Gram–Schmidt Orthogonalization: Production of n-Propyl Propionate in a Simulated Moving-Bed Reactor: A Case Study

“…Finite differences are a simple but common approximation for calculating sensitivity information from a variety of sources, including sensors. − Therefore, a similar strategy is employed here, but including the scaling factors, as shown in eq for a more effective comparison of derivatives. , …”

“…The finite differences have been successfully applied in the literature to deal with raw data to address several problems such as model identification, data regularization, and measurement correction. ,, For example, the finite differences were applied by Drakopoulos and Megalooikonomou to regularize data pipelines in the processing of biomedical information. The referred work has demonstrated the efficiency of finite differences to deal with the regularization of raw data pipelines.…”