{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T21:31:02Z","timestamp":1793482262342,"version":"4.5.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"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:p>Accurate identification of coffee fruit maturity is critical for optimizing harvest timing and ensuring bean quality, but manual inspection is time-consuming and prone to subjectivity. Automated visual detection faces challenges including subtle color differences among maturity stages, frequent occlusions within fruit clusters, variable lighting, and abundant small-scale targets. In this paper, we propose SSViT-YOLOv11, an improved YOLOv11n-based framework that integrates Single Scale Vision Transformer (SSViT) into the backbone and refines multi-scale feature fusion to enhance context modeling and small-object representation. The C3K2 modules in YOLOv11n are integrated with Arbitrary Kernel Convolution (AKConv) and multi-scale convolutional attention (MSCA) is added in the head, effectively improving detection accuracy and rendering the model more lightweight. Experimental results show that SSViT-YOLOv11 achieves superior performance across multiple evaluation metrics. Specifically, the model attains a precision of 81.1%, a recall of 77.4%, and a mean Average Precision (mAP@50) of 84.54%, while operating at 23 FPS and requiring only 2.16 million parameters. These results indicate that the proposed model offers a favorable balance of accuracy, inference speed, and model compactness, making it well suited for assisting farmers in coffee fruit maturity assessment.<\/jats:p>","DOI":"10.3389\/fpls.2025.1691643","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T06:25:46Z","timestamp":1764570346000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["SSViT-YOLOv11: fusing lightweight YOLO &amp; ViT for coffee fruit maturity detection"],"prefix":"10.3389","volume":"16","author":[{"given":"Yifan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiudong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuze","family":"Geng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyi","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"8126","DOI":"10.3390\/s23198126","article-title":"A deep learning approach for precision viticulture, assessing grape maturity via YOLOv7","volume":"23","author":"Badeka","year":"2023","journal-title":"Sensors"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.3390\/agriculture13091812","article-title":"A lightweight pest detection model for drones based on transformer and super-resolution sampling techniques","volume":"13","author":"Bai","year":"2023","journal-title":"Agriculture"},{"key":"B3","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3390\/agronomy14020327","article-title":"ViT-smartAgri: vision transformer and smartphone-based plant disease detection for smart agriculture","volume":"14","author":"Barman","year":"2024","journal-title":"Agronomy"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1590\/1678-992x-2022-0064","article-title":"Detection of coffee fruits on tree branches using computer vision","volume":"80","author":"Bazame","year":"2023","journal-title":"Scientia Agricola"},{"key":"B5","first-page":"62","article-title":"Research on optimization of lightweight transformer architecture for cherry tomato picking and recognition","volume":"55","author":"Bo","year":"2024","journal-title":"Trans. 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