人工智能
分级(工程)
分类器(UML)
机器学习
支持向量机
圆度(物体)
计算机科学
扎梅斯
分割
数学
模式识别(心理学)
农学
工程类
生物
几何学
土木工程
作者
Esa Prakasa,Dicky Rianto Prajitno,Amin Nur,Kukuh Aji Sulistyo,Ema Rachmawati
出处
期刊:2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
日期:2021-09-29
标识
DOI:10.1109/3ict53449.2021.9581464
摘要
Corn yield improvement program aims to attain continuous national self-sufficiency. The program needs to be supported by the availability of food resources, including high-quality corn seeds. In corn seed production, grading is one of the factors that affect the quality of corn seeds. The grading process is conducted manually by visual observations of workers. This process tends to be subjective and ineffective. Some corn seed factories use sieve machines to do grading by seed size. In this paper, an imaging-based classification system is proposed to perform corn seeds (BIMA-20 URI Hybrid) grading of two classes, which are categorised as good and bad. Three different methods are studied in the paper. The methods are respectively based on (1) shape, colour, and size features, (2) seed roundness, and (3) deep learning approach. Images data is acquired in a group of five corn kernels. Region-of-interest (ROI) segmentation is performed to select every single seed from the group image. Features values are then extracted from a single seed image and used as a classification parameter. The F 1 score of the proposed classification system, roundness differentiation, and model training performance can be used to show the categorisation capability. The deep learning approach has achieved the best F 1 score among the other proposed techniques. The best F 1 value, 0.983, is obtained at the ResNet-50 implementation. In separated observation, Method 6 (Size and Colour), Method 7 (Size, Shape, and Colour), Roundness, and ResNet-50 are represented as the best model for each group method. These methods reach F 1 scores more than 0.9, except the roundness parameter. The F 1 score of the roundness parameter is found at 0.854. Additional parameters might be required by the method based on the roundness feature for improving its final performance.
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