Using different classification models in wheat grading utilizing visual features

Abstract

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

Discriminating Healthy Wheat Grains from Grains Infected with Fusarium graminearum Using Texture Characteristics of Image-Processing Technique, Discriminant Analysis, and Support Vector Machine Methods

“…A set of these criteria is based on the statistical moment of the intensity histogram values. The term "central moments" (or average centered moment) [3] used to describe the histogram figure is as follows:…”

“…As the histogram is assumed to be normal, the sum of all its components is 1, and consequently, we obtain from the previous equation that µ 0 = 1 and µ 1 = 0. The second moment is the variance [3]: In this study, a total of eight texture characteristics of intensity histogram diagram and six texture characteristics based on the contingency matrix were obtained from the image of each split grain, as shown in Tables 1 and 2, respectively [8].…”

“…Therefore, its health condition is of paramount importance. In order to increase production of this vital plant, it is important to pay attention to discriminating diseases and pests that threaten wheat health [3,4]. One of the most important wheat diseases with a lot of damages to this product is FHB (Fusarium head blight) [1].…”

Mobile App for Grading of Peanuts using Fuzzy Inference System

“…Result shows maximum adulteration level classification accuracy of 93.31% is attained using BPNN classifier with PCA based feature selection method. Zahra Basati, et al [10] developed an algorithm for identification and classification of healthy and bug damaged wheat grains is done using Artificial Neural Network (ANN), Decision Tree and Discriminant Analysis Classifiers. CfssubsetEval evaluator of Weka tool was used for selecting significant eleven features out of 25 features (CF, TF and SF).…”

Detection of sunn pest‐damaged wheat grains using artificial bee colony optimization‐based artificial intelligence techniques

“…The success rate of the method was reported as 75%. In 2018, the classification of wheats using 25 features, including nine color features, ten morphological features and six textural features, was presented by Basati et al . They obtained 90.20% classification accuracy with a decision tree algorithm and 87.46% accuracy with an ANN classifier.…”