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Due to the complex conditions, such as different growth periods and occlusion among leaves and fruits, detecting fruits in natural environments is a considerable challenge. A rapid citrus recognition method by improving the state-of-the-art You Only Look Once version 4 (YOLOv4) detector is proposed in this paper. Kinect V2 camera was used to collect RGB images of citrus trees. The Canopy algorithm and the K-Means++ algorithm were then used to automatically select the number and size of the prior frames from these RGB images. An improved YOLOv4 network structure was proposed to better detect smaller citrus under complex backgrounds. Finally, the trained network model was used for sparse training, pruning unimportant channels or network layers in the network, and fine-tuning the parameters of the pruned model to restore some of the recognition accuracy. The experimental results show that the improved YOLOv4 detector works well for detecting different growth periods of citrus in a natural environment, with an average increase in accuracy of 3.15% (from 92.89% to 96.04%). This result is superior to the original YOLOv4, YOLOv3, and Faster R-CNN. The average detection time of this model is 0.06\u2009s per frame at 1920\u2009\u00d7\u20091080 resolution. The proposed method is suitable for the rapid detection of the type and location of citrus in natural environments and can be applied to the application of citrus picking and yield evaluation in actual orchards.<\/jats:p>","DOI":"10.1155\/2020\/8859237","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T03:05:05Z","timestamp":1606359905000},"page":"1-13","source":"Crossref","is-referenced-by-count":31,"title":["Detecting Citrus in Orchard Environment by Using Improved YOLOv4"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0714-2234","authenticated-orcid":true,"given":"Wenkang","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science & Information Engineering, Guangxi Normal University, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-9418","authenticated-orcid":true,"given":"Shenglian","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science & Information Engineering, Guangxi Normal University, Guilin 541004, China"},{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guilin 541004, China"}]},{"given":"Binghao","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangxi Academy of Specialty Crops\/Guangxi Citrus Breeding and Cultivation Engineering Technology Center, Guilin 541004, China"}]},{"given":"Guo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science & Information Engineering, Guangxi Normal University, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9708-2521","authenticated-orcid":true,"given":"Tingting","family":"Qian","sequence":"additional","affiliation":[{"name":"Agricultural Information Institutes of Science and Technology, Shanghai Academy of Agriculture Sciences, Shanghai 201403, China"}]}],"member":"311","reference":[{"key":"1","first-page":"825","article-title":"Comparative analysis of chemical composition, antimicrobial and antioxidant activity of citrus essential oils from the main cultivated varieties in China","volume-title":"Lebensmittel-Wissenschaft & Technologie","author":"J. 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