{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T02:54:58Z","timestamp":1777517698661,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:00:00Z","timestamp":1606348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Ministry of Agriculture, Food and Rural Affairs","award":["Golden Seed Project"],"award-info":[{"award-number":["Golden Seed Project"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, conventional machine learning and deep leaning approaches were evaluated using X-ray imaging techniques for investigating the internal parameters (endosperm and air space) of three cultivars of watermelon seed. In the conventional machine learning, six types of image features were extracted after applying different types of image preprocessing, such as image intensity and contrast enhancement, and noise reduction. The sequential forward selection (SFS) method and Fisher objective function were used as the search strategy and feature optimization. Three classifiers were tested (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbors algorithm (KNN)) to find the best performer. On the other hand, in the transfer learning (deep learning) approaches, simple ConvNet, AlexNet, VGG-19, ResNet-50, and ResNet-101 were used to train the dataset and class prediction of the seed. For the supervised model development (both conventional machine learning and deep learning), the germination test results of the samples were used where the seeds were divided into two classes: (1) normal viable seeds and (2) nonviable and abnormal viable seeds. In the conventional classification, 83.6% accuracy was obtained by LDA using 48 features. ResNet-50 performed better than other transfer learning architectures, with an 87.3% accuracy which was the highest accuracy in all classification models. The findings of this study manifested that transfer learning is a constructive strategy for classifying seeds by analyzing their morphology, where X-ray imaging can be adopted as a potential imaging technique.<\/jats:p>","DOI":"10.3390\/s20236753","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T09:04:15Z","timestamp":1606381455000},"page":"6753","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"given":"Mohammed Raju","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea"}]},{"given":"Jannat","family":"Yasmin","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea"}]},{"given":"Eunsung","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-3649","authenticated-orcid":false,"given":"Geonwoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA"}]},{"given":"Moon S.","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA"}]},{"given":"Collins","family":"Wakholi","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea"}]},{"given":"Changyeun","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8397-9853","authenticated-orcid":false,"given":"Byoung-Kwan","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea"},{"name":"Department of Smart Agriculture System, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bewley, J.D., and Black, M. 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