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A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass

文献类型： 外文期刊

作者： Fan, Xinyan 1 ; Kawamura, Kensuke 2 ; Guo, Wei 3 ; Xuan, Tran Dang 1 ; Lim, Jihyun 4 ; Yuba, Norio 5 ; Kurokawa, Yuzo; 1 ;

作者机构： 1.Hiroshima Univ, Grad Sch Int Dev & Cooperat IDEC, Hiroshima, Japan

2.JIRCAS, Tsukuba, Ibaraki, Japan

3.Univ Tokyo, Inst Sustainable Agroecosyst Serv, Tokyo, Japan

4.Natl Agr & Food Res Org, Western Reg Agr Res Ctr, Tsukuba, Ibaraki, Japan

5.Hiroshima Prefectural Technol Res Inst, Higashihiroshima, Hiroshima, Japan

6.Hiroshima Univ, Setouchi Field Sci Ctr, Grad Sch Biosphere Sci, Hiroshima, Japan

7.Hiroshima Univ, Grad Sch Biosphere Sci, Hiroshima, Japan

8.Chinese Acad Trop Agr Sci, Trop Crops Genet Resources Inst, Haikou, Hainan, Peoples R China

9.Yamanashi Prefectural Govt, Mt Fuji Res Inst, Kofu, Yamanashi, Japan

10.Hiroshima Univ, Dept Informat Engn, Grad Sch Engn, Hiroshima, Japan

关键词： Digital camera;Time-series data;Image processing;Agronomic parameters;Growth modeling;Quick growth stage

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2018 年 144 卷

页码：

收录情况： SCI

摘要： Crop growth stage is critical for making decisions in nutrient management and for evaluating crop productivity. In this study, a simple visible and near-infrared (V-NIR) camera system was developed for monitoring the leaf area index (LAI) and quantifying the quick growth stage (QGS) of Italian ryegrass. RAW format images in the red, green and NIR channels over two growing seasons of 2014-15 and 2015-16 were captured hourly each day by the V-NIR camera system installed in three Italian ryegrass fields at the farm of Hiroshima University. Multiple linear regression (MLR) models that predict the forage LAI from the imagery data were calibrated and validated, with high coefficient of determination (R-2 = 0.79) and low root-mean-square error (RMSE = 1.09) between the measured and predicted LAIs. The predicted LAI to which three vegetation indices were compared was fitted against a logistic model to extract forage QGS from smoothed time-series data under various micro meteorological and nutrient conditions. The result shows the time-series data of LAI can be applied for monitoring seasonal changes regardless of the environmental conditions. The RMSE of the predicted phenology dates against the field-measured LAI was 0.58 and 5.2 days for the start- and end-QGS, respectively, under the high yield condition in season 1. However, in season 2, only the start-QGS was identifiable, with an RMSE of 2.65 days under the nutritional stress condition. The forage LAI and QGS were predicted and identified with acceptable accuracy and reliability, which suggests that the V-NIR camera system can be employed as a cost-effective approach for monitoring seasonal changes in crop growth, aiding in better personalized crop and nutrient management.

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