{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T07:01:48Z","timestamp":1790146908624,"version":"4.0.1"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Dense leaf occlusion and fruit\u2013leaf feature confusion hinder simultaneous extraction of pineapple instance contours and appearance-based ripeness classification in field images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dataset of 6,019 field images and 9,393 polygon-annotated Bali pineapple instances was constructed, with fruits categorized as Unripe, Partially ripe, or Ripe according to standardized external-appearance criteria. PineRipeSeg, a lightweight YOLO11n-seg-based network, was developed. Its principal contribution is the proposed Texture-Edge Decoupling Attention (TEDA) module, which exploits the contrast between multidirectional pineapple-eye textures and elongated leaf edges to enhance fruit-surface features and suppress leaf interference. GhostConv reduces redundant computation, whereas an intersection-over-union (IoU) loss with scaled auxiliary boxes, termed Inner-IoU, refines occluded-fruit localization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Mean average precision (mAP) reached 98.1% for boxes at IoU 0.5, 98.0% for masks at IoU 0.5, and 82.2% for masks across IoU 0.5 -0.95, exceeding YOLO11n-seg by 5.0, 5.2, and 3.4 percentage points, respectively. Missed detections decreased by 77.8%. The model required 8.7 giga floating-point operations (GFLOPs), occupied 5.1 MB, and processed 121 frames per second (FPS). Comparative, ablation, repeated-run, challenging-scene, and heatmap analyses supported its effectiveness under occlusion and similarly colored backgrounds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>PineRipeSeg provides a lightweight two-dimensional method for occlusion-aware hybrid instance segmentation and external ripeness assessment in field environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1953890","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:19:50Z","timestamp":1790144390000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PineRipeSeg: a lightweight texture-edge-aware network for pineapple instance segmentation and ripeness classification in field environments"],"prefix":"10.3389","volume":"17","author":[{"given":"Hang","family":"Shi","sequence":"first","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University","place":["Daqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University","place":["Daqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University","place":["Daqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changxi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University","place":["Daqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University","place":["Daqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailiang","family":"Li","sequence":"additional","affiliation":[{"name":"South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Key Laboratory of Tropical Fruit Biology, Ministry of Agriculture and Rural Affairs","place":["Zhanjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University","place":["Daqing, China"]},{"name":"Heilongjiang Engineering Technology Research Center for Potato Mechanization","place":["Daqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1038\/s41598-022-12096-6","article-title":"Using deep learning to identify maturity and 3D distance in pineapple fields","volume":"12","author":"Chang","year":"2022","journal-title":"Sci. Rep."},{"key":"B2","doi-asserted-by":"publisher","first-page":"175","DOI":"10.11975\/j.issn.1002-6819.202509148","article-title":"Orchard pear fruit growth-quality detection and binocular localization method based on improved YOLOv8n","volume":"42","author":"Chen","year":"2026","journal-title":"Trans. Chin. Soc Agric. Eng."},{"key":"B3","first-page":"1290","article-title":"Masked-attention Mask Transformer for universal image segmentation","author":"Cheng","year":"2022"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1709939","article-title":"SRC-YOLOv8n: A lightweight framework for fine-grained apple leaf disease detection with spatial detail preservation and multi-scale feature enhancement","volume":"16","author":"Cui","year":"2026","journal-title":"Front. Plant Sci."},{"key":"B5","first-page":"11963","article-title":"Scaling up your kernels to 31 \u00d7 31: Revisiting large kernel design in CNNs","author":"Ding","year":"2022"},{"key":"B6","first-page":"1580","article-title":"GhostNet: More features from cheap operations","author":"Han","year":"2020"},{"key":"B7","first-page":"23.1","article-title":"A combined corner and edge detector","author":"Harris","year":"1988"},{"key":"B8","first-page":"2961","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture14070975","article-title":"Research status and development trend of key technologies for pineapple harvesting equipment: A review","volume":"14","author":"He","year":"2024","journal-title":"Agriculture"},{"key":"B10","first-page":"13713","article-title":"Coordinate attention for efficient mobile network design","author":"Hou","year":"2021"},{"key":"B11","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"B12","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12060856","article-title":"A real-time apple targets detection method for picking robot based on ShufflenetV2-YOLOX","volume":"12","author":"Ji","year":"2022","journal-title":"Agriculture"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3390\/plants13213001","article-title":"Implementing real-time image processing for radish disease detection using hybrid attention mechanisms","volume":"13","author":"Ji","year":"2024","journal-title":"Plants"},{"key":"B15","doi-asserted-by":"publisher","first-page":"4637","DOI":"10.1109\/TON.2026.3677371","article-title":"MoE\u00b2: Optimizing collaborative inference for edge large language models","volume":"34","author":"Jin","year":"2026","journal-title":"IEEE Trans. Netw."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105302","article-title":"Fruit detection, segmentation and 3D visualisation of environments in apple orchards","volume":"171","author":"Kang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3390\/app13042691","article-title":"A pineapple target detection method in a field environment based on improved YOLOv7","volume":"13","author":"Lai","year":"2023","journal-title":"Appl. Sci."},{"key":"B18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10681-022-03030-y","article-title":"Current status of pineapple breeding, industrial development, and genetics in China","volume":"218","author":"Li","year":"2022","journal-title":"Euphytica"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy14112526","article-title":"FEI-YOLO: A lightweight soybean pod-type detection model","volume":"14","author":"Li","year":"2024","journal-title":"Agronomy"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture15151697","article-title":"Enhanced YOLO11n-Seg with attention mechanism and geometric metric optimization for instance segmentation of ripe blueberries in complex greenhouse environments","volume":"15","author":"Luo","year":"2025","journal-title":"Agriculture"},{"key":"B21","doi-asserted-by":"publisher","first-page":"8317","DOI":"10.1109\/TIP.2025.3609135","article-title":"Meta-TIP: An unsupervised end-to-end fusion network for multi-dataset style-adaptive threat image projection","volume":"34","author":"Ma","year":"2025","journal-title":"IEEE Trans. Image. Process."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy14122783","article-title":"State-of-the-art techniques for fruit maturity detection","volume":"14","author":"Ma","year":"2024","journal-title":"Agronomy"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108298","article-title":"Transforming unmanned pineapple picking with spatio-temporal convolutional neural networks","volume":"214","author":"Meng","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"B24","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1109\/TIP.2025.3648497","article-title":"FCS-edNET: Exploring magnetic particle imaging deblurring with neural network","volume":"35","author":"Peng","year":"2026","journal-title":"IEEE Trans. Image. Process."},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3390\/horticulturae9080889","article-title":"Nondestructive detecting maturity of pineapples based on visible and near-infrared transmittance spectroscopy coupled with machine learning methodologies","volume":"9","author":"Qiu","year":"2023","journal-title":"Horticulturae"},{"key":"B26","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1002\/rob.22230","article-title":"Towards autonomous selective harvesting: A review of robot perception, robot design, motion planning and control","volume":"41","author":"Rajendran","year":"2024","journal-title":"J. Field Robot."},{"key":"B27","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"B28","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.postharvbio.2023.112706","article-title":"Nondestructive online measurement of pineapple maturity and soluble solids content using visible and near-infrared spectral analysis","volume":"209","author":"Semyalo","year":"2024","journal-title":"Postharvest. Biol. Technol."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111162","article-title":"Towards mechanized harvesting of pineapples: A masked self-attention instance segmentation network and pineapple detection dataset","volume":"156","author":"Shan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107788","article-title":"An edge-guided method to fruit segmentation in complex environments","volume":"208","author":"Sheng","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1558378","article-title":"Real-time detection of Chinese cabbage seedlings in the field based on YOLO11-CGB","volume":"16","author":"Shi","year":"2025","journal-title":"Front. Plant Sci."},{"key":"B33","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1109\/TCCN.2025.3614368","article-title":"WPMNet: Weight pruning and parameter quantization assisted multi-scale lightweight network for interference recognition","volume":"12","author":"Wang","year":"2026","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"B34","doi-asserted-by":"publisher","first-page":"4261","DOI":"10.1038\/s41598-024-54540-9","article-title":"Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture15131353","article-title":"Tomato yield estimation using an improved lightweight YOLO11n network and an optimized region tracking-counting method","volume":"15","author":"Wang","year":"2025","journal-title":"Agriculture"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1503256","article-title":"Tomato ripeness detection and fruit segmentation based on instance segmentation","volume":"16","author":"Wei","year":"2025","journal-title":"Front. Plant Sci."},{"key":"B37","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"B38","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s11633-024-1517-1","article-title":"Key technologies for machine vision for picking robots: Review and benchmarking","volume":"22","author":"Xiao","year":"2025","journal-title":"Mach. Intell. Res."},{"key":"B39","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1109\/TIP.2026.3661417","article-title":"PestScope: Exclusion-aware large multimodal model for fine-grained agricultural pest segmentation","volume":"35","author":"Yang","year":"2026","journal-title":"IEEE Trans. Image. Process."},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture14010029","article-title":"MSGV-YOLOv7: A lightweight pineapple detection method","volume":"14","author":"Zhang","year":"2024","journal-title":"Agriculture"}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1953890\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:19:54Z","timestamp":1790144394000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1953890\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":40,"alternative-id":["10.3389\/fpls.2026.1953890"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1953890","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]},"article-number":"1953890"}}