{"title":"基于人工智能的二维和三维葡萄园产量预测系统的对比分析","authors":"Dhanashree Barbole, Parul M. Jadhav","doi":"10.4081/jae.2023.1545","DOIUrl":null,"url":null,"abstract":"Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, costly, and its accuracy is dependent on the scale of the sample. To overcome these problems, hybrid approaches of Computer Vision (CV), Deep Learning (DL) and Machine Learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image is employed using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy upto 94.81% and yield prediction accuracy upto 99.58%.","PeriodicalId":48507,"journal":{"name":"Journal of Agricultural Engineering","volume":"547 ","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence\",\"authors\":\"Dhanashree Barbole, Parul M. Jadhav\",\"doi\":\"10.4081/jae.2023.1545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, costly, and its accuracy is dependent on the scale of the sample. To overcome these problems, hybrid approaches of Computer Vision (CV), Deep Learning (DL) and Machine Learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image is employed using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy upto 94.81% and yield prediction accuracy upto 99.58%.\",\"PeriodicalId\":48507,\"journal\":{\"name\":\"Journal of Agricultural Engineering\",\"volume\":\"547 \",\"pages\":\"0\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Agricultural Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4081/jae.2023.1545\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agricultural Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4081/jae.2023.1545","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
Comparative analysis of 2D and 3D vineyard yield prediction system using artificial intelligence
Traditional techniques for estimating the weight of clusters in a winery, generally consist of manually counting the variety of clusters per vine, and scaling by means of the entire variety of vines. This method can be arduous, costly, and its accuracy is dependent on the scale of the sample. To overcome these problems, hybrid approaches of Computer Vision (CV), Deep Learning (DL) and Machine Learning (ML) based vineyard yield prediction systems are proposed. Self-prepared datasets are used for comparative analysis of 2D and 3D yield prediction systems for vineyards. DL-based approach for segmentation operation on an RGB-D image dataset created with the D435I camera is used along with the ML-based weight prediction technique of grape clusters present in the single image is employed using these datasets. A comparative analysis of the DL-based Keras regression model and various ML-based regression models for the weight prediction task is taken into account, and finally a prediction model is proposed to estimate the yield of the entire vineyard. The analysis shows improved performance with the 3D vineyard yield prediction system compared to the 2D vineyard yield prediction system with grape cluster segmentation pixel accuracy upto 94.81% and yield prediction accuracy upto 99.58%.
期刊介绍:
The Journal of Agricultural Engineering (JAE) is the official journal of the Italian Society of Agricultural Engineering supported by University of Bologna, Italy. The subject matter covers a complete and interdisciplinary range of research in engineering for agriculture and biosystems.