Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination

Abstract

Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage.

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

“…A biological neural network consists of many interconnected biological neurons. ANNs are formed by simple units of processing, called neurons [34][35][36][37].…”

“…Implementation of Artificial Neural Networks for different research proved to be very efficient and accurate. ANNs are characterized by very high accuracy in comparison with other mathematical models (regression models, general linear models, regression trees) [1,5,6,35,36,[40][41][42][43]. For example, Kasantikul et al [36] proposed a combination of an ANN and a particle filter (PF) to estimate wind speed.…”

“…ANNs are used in various scientific fields including, for example, bioinformatics, biochemistry, medicine, meteorology, economic sciences, robotics, aquaculture, food security and climatology. ANNs are also used in agriculture, agrophysics or agricultural engineering [1,35,[40][41][42][43]47].…”

Decision Support System for the Production of Miscanthus and Willow Briquettes

“…The term artificial neural network refers to a computational and machine learning technique [40][41][42][43]. One type of neural networks are multilayer perceptron neural networks (MLPs).…”

“…ANNs are universal nonlinear approximators. Implementation of artificial neural networks for different research proved to be very efficient and accurate [20,40,43,44,[47][48][49][50][51][52].…”