{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T06:04:51Z","timestamp":1776578691247,"version":"3.51.2"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agriculture and Food Agency, Council of Agriculture, Executive Yuan, Taiwan","award":["110AS-3.1.1-FD-Z4"],"award-info":[{"award-number":["110AS-3.1.1-FD-Z4"]}]},{"name":"Agriculture and Food Agency, Council of Agriculture, Executive Yuan, Taiwan","award":["NSTC 111-2410-H-035-020-"],"award-info":[{"award-number":["NSTC 111-2410-H-035-020-"]}]},{"name":"Agriculture and Food Agency, Council of Agriculture, Executive Yuan, Taiwan","award":["NSTC 111-2119-M-008-006-"],"award-info":[{"award-number":["NSTC 111-2119-M-008-006-"]}]},{"name":"the National Science and Technology Council, Taiwan","award":["110AS-3.1.1-FD-Z4"],"award-info":[{"award-number":["110AS-3.1.1-FD-Z4"]}]},{"name":"the National Science and Technology Council, Taiwan","award":["NSTC 111-2410-H-035-020-"],"award-info":[{"award-number":["NSTC 111-2410-H-035-020-"]}]},{"name":"the National Science and Technology Council, Taiwan","award":["NSTC 111-2119-M-008-006-"],"award-info":[{"award-number":["NSTC 111-2119-M-008-006-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Early production warnings are usually labor-intensive, even with remote sensing techniques in highly intensive but fragmented growing areas with various phenological stages. This study used high-resolution unmanned aerial vehicle (UAV) images with a ground sampling distance (GSD) of 3 cm to detect the plant body of pineapples. The detection targets were mature fruits mainly covered with two kinds of sun protection materials\u2014round plastic covers and nets\u2014which could be used to predict the yield in the next two to three months. For round plastic covers (hereafter referred to as wearing a hat), the Faster R-CNN was used to locate and count the number of mature fruits based on input image tiles with a size of 256 \u00d7 256 pixels. In the case of intersection-over-union (IoU) &gt; 0.5, the F1-score of the hat wearer detection results was 0.849, the average precision (AP) was 0.739, the precision was 0.990, and the recall was 0.743. We used the Mask R-CNN model for other mature fruits to delineate the fields covered with nets based on input image tiles with a size of 2000 \u00d7 2000 pixels and a mean IoU (mIoU) of 0.613. Zonal statistics summed up the area with the number of fields wearing a hat and covered with nets. Then, the thresholding procedure was used to solve the potential issue of farmers\u2019 harvesting in different batches. In pineapple cultivation fields, the zonal results revealed that the overall classification accuracy is 97.46%, and the kappa coefficient is 0.908. The results were expected to demonstrate the critical factors of yield estimation and provide researchers and agricultural administration with similar applications to give early warnings regarding production and adjustments to marketing.<\/jats:p>","DOI":"10.3390\/rs15030814","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T05:33:53Z","timestamp":1675229633000},"page":"814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Pineapples\u2019 Detection and Segmentation Based on Faster and Mask R-CNN in UAV Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8660-298X","authenticated-orcid":false,"given":"Yi-Shiang","family":"Shiu","sequence":"first","affiliation":[{"name":"Department of Urban Planning and Spatial Information, Feng Chia University, Taichung 407802, Taiwan"}]},{"given":"Re-Yang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Land Management, Feng Chia University, Taichung 407802, Taiwan"}]},{"given":"Yen-Ching","family":"Chang","sequence":"additional","affiliation":[{"name":"Chung Hsing Surveying Co., Ltd., Taichung 403006, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations (2022, October 01). 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