Corn quality identification using image processing with k-nearest neighbor classifier based on color and texture features

International Conference on Green Agro-industry and Bioeconomy

农业科学;工业技术（总论）;经济学

Effendi, M.^1 ; Jannah, M.^1 ; Effendi, U.^1

Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Brawijaya, Malang, Indonesia^1

关键词: Color and texture features;    Distance calculation;    Drying time;    K-nearest neighbor classifier;    k-NN algorithm;    Level of consistencies;    Nearest neighbors;    Quality identifications;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/230/1/012066/pdf
DOI  :  10.1088/1755-1315/230/1/012066

来源: IOP

【 摘 要 】

Corn is food crop commodity that is widely used, including as raw material for animal feed. Determination of corn quality at the farm level is often associated with drying time. This method has weaknesses, namely low efficiency, objectivity and level of consistency and also can lead to conflicts between traders and farmers. This study aims to identify the quality of corn using digital image processing based on color and texture features. This research uses Pertiwi-3 and Pertiwi-6 corn varieties. The corn quality identification system uses 7 features input (hue, saturation, value, contrast, correlation, energy, homogeneity) and KNN algorithm as classifiers. The number of image data used are 500 images with a test ratio of 70: 30. This research is able to classify the quality of corn into 10 quality categories. The highest accuracy is obtained at 90.00% when the k value (the nearest neighbor) is 5 and the distance calculation method is Cityblock.