Estimation of Fusarium Scab in Wheat Using Machine Vision and a Neural Network

Abstract

A neural network was used to relate color and texture features of wheat samples to damage caused by Fusarium scab infection. A total of 55 color and texture features were extracted from images captured by a machine vision system. Random errors were reduced by using average values of features from multiple images of individual samples. A four‐layer backpropagation neural network was used. The percentage of visual scabby kernels (%VSK) estimated by the trained network followed the actual percentage with a correlation coefficient of 0.97; maximum and mean absolute errors were 5.14 and 1.93%, respectively. A comparison between the results by the machine vision‐neural network technique and the human expert panel led to the conclusion that the machine vision‐neural network technique produced more accurate determination of %VSK than the human expert panel.

Classification of Scab– and Other Mold–damaged Wheat Kernels by Near–infrared Reflectance Spectroscopy

“…Sapirstein (1995) reviewed the literature on image analysis of grain through the mid 1990s. Detection of diseased kernels by image analysis is a relatively recent endeavor, with some published works on corn (Ng et al, 1998) and wheat (Ruan et al, 1998;Luo et al, 1999) available. In Ruan's study, neural networks were used on color and textural features of bulk samples to develop a model for wheat scab detection.…”

Semantic Indexing based Remote Sensing Image Retrieval: An Intelligent Decomposition Approach

“…The review of recent research works has demonstrated the advancements and progress of various content based remote sensing images retrieval systems in the recent past, however state-of-the-art works [24] [26] [27] focus mainly on semantic based image retrieval concepts, especially in remote sensing archive [26] [27]. Despite lot of problems have been addressed in the literature [25], developing a wellstructured intelligent feature extraction methods are considered to be the most complex challenge that prevails in the current system.…”

Quality Inspection and Grading of Agricultural and Food Products by Computer Vision-a Review

“…The relationship between colour and texture features of wheat samples to scab infection rate was studied using a neural network method [63]. It was found that the infection rates estimated by the system followed the actual ones with a correlation coefficient of 0.97 with human panel assessment and maximum and mean absolute errors of 5 and 2%, respectively.…”