Computer vision based corn kernel quality evaluation: Traditional versus machine learning
type: CreativeWork
description: Corn kernel quality evaluation is a trivial task for experienced farmers and agriculture researchers, but it becomes tricky if we try to develop a computer vision based automatic solution. In this thesis, we present two approaches for this problem, briefly introduce the data sets corresponding to each method and compare the accuracy between them. We attack the corn kernel quality evaluation problem by two different methods: (1) Evaluate the quality based on the percentage of good corn kernels within the scope by a “percentage” classifier trained with multi-class support vector machine (SVM).(2) Evaluate the quality by a good corn kernel detector trained with multiple state-of-the-art detectors, specifically Faster R-CNN and Retinanet. We collected two databases for both methods separately: (1) Images of many corn kernel batches containing different percentages of good corn kernels vs.foreign matter randomly placed on a flat surface were taken as both training and testing data for multi-class SVM. (2) Reuse the images taken for the SVM data set and add bounding box annotations to each image following the Microsoft COCO fashion. Our experiments show that multi-class SVM reaches a rank-1 accuracy of 78%, while the deep learning detectors achieved96% precision. While the multi-class SVM approach shows good classification results, deep learning models provide more precise detection results.Unfortunately, previous works are all based on lab environments and there is no benchmark available in this field. Therefore, we consider our work as a baseline for corn kernel quality evaluation.
abstract: Corn kernel quality evaluation is a trivial task for experienced farmers and agriculture researchers, but it becomes tricky if we try to develop a computer vision based automatic solution. In this thesis, we present two approaches for this problem, briefly introduce the data sets corresponding to each method and compare the accuracy between them. We attack the corn kernel quality evaluation problem by two different methods: (1) Evaluate the quality based on the percentage of good corn kernels within the scope by a “percentage” classifier trained with multi-class support vector machine (SVM).(2) Evaluate the quality by a good corn kernel detector trained with multiple state-of-the-art detectors, specifically Faster R-CNN and Retinanet. We collected two databases for both methods separately: (1) Images of many corn kernel batches containing different percentages of good corn kernels vs.foreign matter randomly placed on a flat surface were taken as both training and testing data for multi-class SVM. (2) Reuse the images taken for the SVM data set and add bounding box annotations to each image following the Microsoft COCO fashion. Our experiments show that multi-class SVM reaches a rank-1 accuracy of 78%, while the deep learning detectors achieved96% precision. While the multi-class SVM approach shows good classification results, deep learning models provide more precise detection results.Unfortunately, previous works are all based on lab environments and there is no benchmark available in this field. Therefore, we consider our work as a baseline for corn kernel quality evaluation.
author: Li, Xing
language: en
keywords: Computer Vision, Corn Kernel Quality, Machine Learning, Fast R-CNN, RPN, Faster R-CNN, FPN, Retinanet.
date published: 2018-09-04T20:33:55Z
date modified: 2026-07-22T22:24:38Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/101131