{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:46:33Z","timestamp":1782798393373,"version":"3.54.5"},"reference-count":161,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"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>\n                    Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable\n                    <jats:italic>in-situ<\/jats:italic>\n                    monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity\u2013quality monitoring frameworks for actionable crop load management.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1879872","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T05:37:27Z","timestamp":1782797847000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A review: research progress on intelligent technologies for orchard yield monitoring"],"prefix":"10.3389","volume":"17","author":[{"given":"Shengyi","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University","place":["Zhenjiang, China"]},{"name":"National Digital Agricultural Equipment (AI and Agricultural Robotics) Innovation Sub-Centre, Jiangsu University","place":["Zhenjiang, China"]},{"name":"Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University","place":["Zhenjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University","place":["Zhenjiang, China"]},{"name":"Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University","place":["Zhenjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University","place":["Zhenjiang, China"]},{"name":"National Digital Agricultural Equipment (AI and Agricultural Robotics) Innovation Sub-Centre, Jiangsu University","place":["Zhenjiang, China"]},{"name":"Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University","place":["Zhenjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jizhan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, Jiangsu University","place":["Zhenjiang, China"]},{"name":"National Digital Agricultural Equipment (AI and Agricultural Robotics) Innovation Sub-Centre, Jiangsu University","place":["Zhenjiang, China"]},{"name":"Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University","place":["Zhenjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/rs17040703","article-title":"MangiSpectra: a multivariate phenological analysis framework leveraging UAV imagery and LSTM for tree health and yield estimation in mango orchards","volume":"17","author":"Afsar","year":"2025","journal-title":"Remote Sens."},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.868831","article-title":"Advances in citrus flowering: a review","volume":"13","author":"Agust\u00ed","year":"2022","journal-title":"Front. 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