Autonomous Wheat Seed Type Classifier System

Abstract

After harvesting wheat, the main concern is classifying wheat seeds according to their quality, size, variety and etc. there are different procedures to measure parameters and analyzing wheat seeds but they are time-consuming and error-prone. An automated system is developed being capable to analyze and classify wheat seeds faster with higher confidence level based on defined attributes, the system uses popular K-means clustering algorithm. The base of K-means is established on squared error. Several points are given as inputs to algorithm and then they are assigned to k clusters according to distance to the centroids, each point is included in cluster which centroid is nearest to that point. A wheat dataset taken from UCI Machine Learning Repository is considered by k-means algorithm and results are analyzed. The experimental results on prototype data show the effectiveness of the proposed method.

Buğday Tohumlarının Derin Sinir Ağı Uygulaması ile Sınıflandırılması

“…A method was developed for detecting wheat heading and flowering stages with support vector machine [10]. Wheat species were categorized according to their type by using data mining algorithms such as KNN, Naive Bayes, J48 and multilayer perceptron [11] also, K-means clustering algorithm [12], linear regression [13], Sequential Minimal Optimization (SMO) [14], Multilayer Perceptron, SMO, Navie Bayes, Logistic Classifier [15], Random Forest [16], Artificial Neural Network, Decision Tree and Discernment Analysis Classifiers [17], Complete Gradient Clustering Algorithm [18], principal component analysis and multivariate factor analysis [19], Artificial Neural Network (ANN) and Extreme Machine Learning [20] . A spike detection method was developed for images of wheat plants by using a method based on neural network [21].…”