A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period
文献类型： 外文期刊
作者： Qian, Jianping 1 ; Xing, Bin 1 ; Wu, Xiaoming 1 ; Chen, Meixiang 1 ; Wang, Yan'an 2 ;
作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Shuguang Huayuan Middle Rd 11, Beijing 100097, Peoples R China
2.Shandong Agr Univ, Coll Life Sci, State Key Lab Crop Biol, Tai An 271018, Shandong, Peoples R China
关键词： potential yield prediction model; image processing; mobile phone application development; orchard precision management
期刊名称：SCIENTIA AGRICOLA （ 影响因子：2.137； 五年影响因子：2.618 ）
ISSN： 1678-992X
年卷期： 2018 年 75 卷 4 期
页码：
收录情况： SCI
摘要： Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes a distribution framework consisting of the acquisition of fruit tree images, yield prediction in smartphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). An individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups of data were validated. The R-2 value for the training datasets was 0.996 and the relative root mean-squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284. Furthermore, a yield map with 80 apple trees was generated, and the space distribution of the yield was identified. It provided appreciable decision support for site-specific management.
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