Varietal classification of maize seeds using computer vision and machine learning techniques

Abstract In agriculture, seed sorting is critical for production and marketing purposes. Low‐quality seeds can cause poor plant growth and lead to problems such as disease and low yields. This study uses machine vision and machine learning to develop a rapid detection and classification method for maize ( Zea mays L.) seeds based on variety purity. A computer vision system was designed to recognize five varieties of maize seeds. Halogen lamps were applied for illumination and a high‐resolution RGB camera was used to acquire images of 8,080 maize seeds in the laboratory. An image processing algorithm was proposed to extract 16 important features (12 dimensional and 4 of shape) from the maize seed images, and a user‐friendly interface was developed using a MATLAB graphical user interface (GUI). Multilayer perceptron (MLP), decision tree (DT), linear discrimination (LDA), Naive Bayes (NB), support vector machine (SVM), k ‐nearest neighbors (KNN), and AdaBoost algorithm were used to develop the varietal classification model. The optimal model parameters were obtained with 10‐fold cross‐validation, and the performance metrics were compared. The names of the maize varieties were marked in the GUI. The overall classification accuracy was determined as 96.26, 94.95, 95.97, 93.97, 96.46, 95.59, and 95.31% for MLP, DT, LDA, NB, SVM, KNN, and AdaBoost, respectively. The SVM classification model obtained the highest accuracy for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 93.07, 98.95, 96.15, 89.65, and 99.22%, respectively. The classification results satisfy the needs of producers and consumers.