Computer vision based corn kernel quality evaluation: Traditional versus machine learning

Computer Vision, Corn Kernel Quality, Machine Learning, Fast R-CNN, RPN, Faster R-CNN, FPN, Retinanet.

Li, Xing ; Ahuja ; Narendra

关键词: Computer Vision, Corn Kernel Quality, Machine Learning, Fast R-CNN, RPN, Faster R-CNN, FPN, Retinanet.;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101131/LI-THESIS-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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 basedonthepercentageofgoodcornkernelswithinthescopebya“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 collectedtwodatabasesforbothmethodsseparately:(1)Imagesofmany corn kernel batches containing different percentages of good corn kernels vs.foreign matter randomly placed on a flat surface were taken as both training andtestingdataformulti-classSVM.(2)Reusetheimagestakenforthe SVMdata setandaddboundingboxannotationstoeachimagefollowing 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. Whilethemulti-classSVMapproachshowsgoodclassification 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.