{"title":"果树芒果果实检测计数系统研究","authors":"Romil Mahajan, Ambarish Haridas, Mohit Chandak, Rudar Sharma, Charanjeet Dadiyala","doi":"10.47164/ijngc.v14i1.1022","DOIUrl":null,"url":null,"abstract":"For yield estimation, it is crucial to achieve quick and precise identification of mango fruits in the natural situations and surroundings. Using imaging with computer vision to accurately detect and count fruits during plant growth is important. It is not just because it is a vital step toward automating procedures like harvesting but also for minimizing labour-intensive human assessments of phenotypic information which can be useful for the farmer. Fruit farmers or cultivators in agriculture would benefit greatly from being able to track and predict production prior to fruit harvest. In order to make the best use of the resources needed for each individual site, such as water use, fertiliser use, and other agricultural chemical compounds. Mango fruit is considered in this paper. A comparative study on Faster R-CNN, YOLOv3 algorithms, and YOLOv4 algorithms, which are widely used in the field of object recognition in the past on various fruits and objects, was conducted to find the best model. The YOLOv4 algorithm was chosen as it was the best technique for mango fruit recognition based on the findings of the above comparative study. A real-time mango fruit detection method utilizing YOLOv4 deep learning algorithm is put forward. The YOLOv4 (You Only Look Once) model was developed under the CSPDarknet53 framework. Also, the number of mangoes in the image or frame was counted and displayed in images as well as videos.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"86 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On Tree Mango Fruit Detection and Counting System\",\"authors\":\"Romil Mahajan, Ambarish Haridas, Mohit Chandak, Rudar Sharma, Charanjeet Dadiyala\",\"doi\":\"10.47164/ijngc.v14i1.1022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For yield estimation, it is crucial to achieve quick and precise identification of mango fruits in the natural situations and surroundings. Using imaging with computer vision to accurately detect and count fruits during plant growth is important. It is not just because it is a vital step toward automating procedures like harvesting but also for minimizing labour-intensive human assessments of phenotypic information which can be useful for the farmer. Fruit farmers or cultivators in agriculture would benefit greatly from being able to track and predict production prior to fruit harvest. In order to make the best use of the resources needed for each individual site, such as water use, fertiliser use, and other agricultural chemical compounds. Mango fruit is considered in this paper. A comparative study on Faster R-CNN, YOLOv3 algorithms, and YOLOv4 algorithms, which are widely used in the field of object recognition in the past on various fruits and objects, was conducted to find the best model. The YOLOv4 algorithm was chosen as it was the best technique for mango fruit recognition based on the findings of the above comparative study. A real-time mango fruit detection method utilizing YOLOv4 deep learning algorithm is put forward. The YOLOv4 (You Only Look Once) model was developed under the CSPDarknet53 framework. Also, the number of mangoes in the image or frame was counted and displayed in images as well as videos.\",\"PeriodicalId\":42021,\"journal\":{\"name\":\"International Journal of Next-Generation Computing\",\"volume\":\"86 1\",\"pages\":\"\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2023-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Next-Generation Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.47164/ijngc.v14i1.1022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Next-Generation Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47164/ijngc.v14i1.1022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
对芒果果实进行快速、准确的鉴定是产量估算的关键。利用计算机视觉成像技术对植物生长过程中的果实进行准确检测和计数是非常重要的。这不仅是因为它是实现收获等过程自动化的重要一步,而且还因为它可以最大限度地减少对农民有用的表型信息的劳动密集型人类评估。果农或农业种植者将从能够在水果收获前跟踪和预测产量中受益匪浅。为了充分利用每个场地所需的资源,例如水的使用,化肥的使用和其他农业化学化合物。本文以芒果果实为研究对象。对比研究过去在物体识别领域广泛使用的Faster R-CNN算法、YOLOv3算法和YOLOv4算法,对各种水果和物体进行识别,寻找最佳模型。基于以上对比研究的结果,选择YOLOv4算法作为芒果果实识别的最佳技术。提出了一种利用YOLOv4深度学习算法的芒果果实实时检测方法。YOLOv4 (You Only Look Once)模型是在CSPDarknet53框架下开发的。此外,图像或帧中的芒果数量被计算并显示在图像和视频中。
For yield estimation, it is crucial to achieve quick and precise identification of mango fruits in the natural situations and surroundings. Using imaging with computer vision to accurately detect and count fruits during plant growth is important. It is not just because it is a vital step toward automating procedures like harvesting but also for minimizing labour-intensive human assessments of phenotypic information which can be useful for the farmer. Fruit farmers or cultivators in agriculture would benefit greatly from being able to track and predict production prior to fruit harvest. In order to make the best use of the resources needed for each individual site, such as water use, fertiliser use, and other agricultural chemical compounds. Mango fruit is considered in this paper. A comparative study on Faster R-CNN, YOLOv3 algorithms, and YOLOv4 algorithms, which are widely used in the field of object recognition in the past on various fruits and objects, was conducted to find the best model. The YOLOv4 algorithm was chosen as it was the best technique for mango fruit recognition based on the findings of the above comparative study. A real-time mango fruit detection method utilizing YOLOv4 deep learning algorithm is put forward. The YOLOv4 (You Only Look Once) model was developed under the CSPDarknet53 framework. Also, the number of mangoes in the image or frame was counted and displayed in images as well as videos.