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oai:www.ideals.illinois.edu:2142/101131 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Ahuja, Narendra Li, Xing 2018-09-04T20:33:55Z 2018-09-04T20:33:55Z 2020-09-05T09:15:09Z 2018-03-27 2018-05 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. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01 The student, Xing Li, accepted the attached license on 2018-03-26 at 16:23. The student, Xing Li, submitted this Thesis for approval on 2018-03-26 at 16:41. This Thesis was approved for publication on 2018-03-27 at 09:24. DSpace SAF Submission Ingestion Package generated from Vireo submission #12089 on 2018-08-31 at 17:17:43 Made available in DSpace on 2018-09-04T20:33:55Z (GMT). No. of bitstreams: 2 LI-THESIS-2018.pdf: 50717914 bytes, checksum: ecc079e8e3693c2b7ee7859ffc14a8e0 (MD5) LICENSE.txt: 4204 bytes, checksum: fd1bf3ed2e556496cfe6b7f962cc2f07 (MD5) Previous issue date: 2018-03-27 Embargo set by: Seth Robbins for item 107214 Lift date: 2020-09-04T20:34:13Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 107214 Lift date: 2020-09-04T20:37:00Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Embargo set by: Seth Robbins for item 107214 Lift date: 2020-09-04T20:42:08Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 107214 on 2020-09-05T09:15:09Z. application/pdf http://hdl.handle.net/2142/101131 en Copyright 2018 Xing Li Computer Vision, Corn Kernel Quality, Machine Learning, Fast R-CNN, RPN, Faster R-CNN, FPN, Retinanet. Computer vision based corn kernel quality evaluation: Traditional versus machine learning text text Electrical & Computer Eng Electrical Engineering University of Illinois at Urbana-Champaign Thesis M.S.