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作物学报

作物学报 ›› 2022, Vol. 48 ›› Issue (5): 1248-1261.doi: 10.3724/SP.J.1006.2022.02065

• 耕作栽培·生理生化 • 上一篇    下一篇

基于无人机和地面图像的田间水稻冠层参数估测与评价

王泽1(), 周钦阳1(), 刘聪1, 穆悦1, 郭威2, 丁艳锋1,*(), 二宫正士1,2,*()   

  1. 1南京农业大学作物表型组学交叉研究中心 / 南京农业大学前沿交叉研究院 / 江苏省现代作物生产协同创新中心 / 现代作物生产省部共建协同创新中心, 中国江苏南京 210095
    2东京大学农学生命科学研究院生态调和农学机构 / 国际田间作物表型研究实验室, 日本东京 188-0002
  • 收稿日期:2020-10-09 接受日期:2021-09-09 出版日期:2022-05-12 网络出版日期:2021-10-19
  • 通讯作者: 丁艳锋,二宫正士
  • 作者简介:王泽, E-mail: 2019101170@njau.edu.cn;
    周钦阳, E-mail: 2019201022@njau.edu.cn第一联系人：**同等贡献
  • 基金资助:
    江苏省科技厅创新能力建设计划项目——作物表型组学研究科学中心项目(BM2018001);中央高校基本科研业务费专项资金项目资助(KYRC202002)

Estimation and evaluation of paddy rice canopy characteristics based on images from UAV and ground camera

WANG Ze1(), ZHOU Qin-Yang1(), LIU Cong1, MU Yue1, GUO Wei2, DING Yan-Feng1,*(), NINOMIYA Seishi1,2,*()   

  1. 1Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production, Jiangsu Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
    2International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan
  • Received:2020-10-09 Accepted:2021-09-09 Published:2022-05-12 Published online:2021-10-19
  • Contact: DING Yan-Feng,NINOMIYA Seishi
  • About author:First author contact:**Contributed equally to this work
  • Supported by:
    Plant Phenomics Research Program of Science and Technology Department of Jiangsu Province(BM2018001);Fundamental Research Funds for the Central Universities(KYRC202002)

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摘要：

田间水稻表型监测可用于分析水稻产量相关性状, 对指导水稻栽培管理以及产量预测具有重要意义。本研究以3种氮肥处理下6个不同栽培品种的水稻为研究对象, 估测并评价了水稻冠层的主要表型参数, 以探讨利用图像分析方法评价多品种及栽培环境下田间水稻长势的适用性。基于无人机和田间固定相机图像, 本研究通过图像处理、三维建模和机器学习自动测算出田间水稻冠层覆盖度、株高、穗数, 并结合实际测量结果进行了精度评价。结果表明: (1) 基于无人机图像使用决策树分类模型提取的水稻冠层图像与人工勾绘结果一致性较好(Qseg均值为0.75, 方差为0.08), 由此计算的冠层覆盖度与人工勾绘计算的冠层覆盖度相关性较高(R2= 0.83, RMSE = 5.36%); (2) 使用冠层高度模型估测的各小区水稻株高均值与田间实测高度均值相关性较高(R2= 0.81, RMSE = 9.81 cm), 但整体呈现低估; (3) 基于地面图像使用决策树分类和形态参数过滤得到的穗数计数结果与实测穗数相关性较高(R2= 0.83, RMSE = 10.99)。总体而言, 结合图像分析算法, 应用低空无人机遥感技术高通量自动化估测水稻冠层覆盖度、株高的精度较高, 而应用地面平台进行稻穗精确识别的潜力很大, 可用于分析氮肥施用量对水稻长势指标的影响及不同品种对氮肥的响应情况, 对田间水稻表型信息的深入挖掘及实际产量预测具有重要意义。

关键词: 水稻表型, 表型平台, 长势参数, 株高, 冠层覆盖度, 穗数计数

Abstract:

Phenotypic monitoring of rice in the field can be used to analyze traits related to rice yield, which is of great significance to guide rice cultivation management and yield prediction. In this study, to explore the applicability of image analysis methods to evaluate rice growth in different fields under multiple cultivars and cultivation environments, we estimated and evaluated the main phenotypic parameters of rice canopy in paddy fields with six different cultivars under three nitrogen treatments. Based on UAV and field fixed camera images, this study used image processing, three-dimensional modeling and machine learning to automatically estimate rice canopy coverage, plant height, and panicle number in the field, and evaluated the accuracy combining with the actual measurement results. The results showed that the rice canopy segmentation based on the decision tree classification model using UAV images were consistent with the manually segmented results (mean value and variance of Qseg was 0.75 and 0.08), and the rice canopy coverage calculated by this method had a relatively high correlation with that calculated by manually segmentation (R2= 0.83, RMSE = 5.36%). The average rice plant height estimated by the canopy height model in each plot had a high correlation with the mean plant height measured in the field (R2= 0.81, RMSE = 9.81 cm), but it was underestimated as a whole. Based on the ground image, the panicle count results obtained by decision tree classification and morphological parameter filter had a relatively high correlation with the measured panicle number (R2= 0.83, RMSE = 10.99). Overall, combined with image analysis algorithm, using low-altitude UAV remote sensing technology to high-throughput and automatically estimate rice canopy coverage and plant height can achieve relatively high accuracy; using image from ground camera to accurately count the rice panicle number is of significant potentiality. The proposed pipeline in this research could be used to analyze nitrogen effect on rice growth status and evaluate nitrogen response of different rice cultivars, and it is of great significance for mining rice field phenotypic information and yield prediction.

Key words: rice phenotype, phenotyping platform, growth parameters, plant height, canopy coverage, panicle counting

图1

试验地点及示意图 A: 试验地点; B: 小区布局。中国地图来源: 标准地图服务系统(GS(2020)3183号)。"

图2

2019年试验小区品种及氮肥处理分布 A1: 中浙优8号; A2: Y两优900; A3: 兆优5431; B1: 南粳9108; B2: 宁粳8号; B3: 武运粳23。灰色区域: N0; 浅绿色: N150; 深绿: N300。"

附图1

2020年试验小区品种及氮肥处理分布 a1：黄华占;a2：Y两优900;a3：中浙优1;a4：甬优1540;b1：宁粳8号;b2：南粳46;b3：9优418;b4：甬优538; 绿色区域：30 kg hm-2 (N1);紫色：10%减氮 270 kg hm-2 (N2);综色：20%减氮 240 kg hm-2 (N3);红色：使用缓释肥的20%减氮 240 kg hm-2 (N4);浅绿色：30%减氮 210 kg hm-2 (N5)。"

图3

基于太阳能的田间作物图像自动采集传输装置"

图4

基于地面固定相机平台和低空无人机平台水稻冠层表型参数估测的总体研究方法路线"

图5

2019年各小区RGB正射影像(A)和冠层表面高度(B)"

图6

稻穗分割及去噪 A: 决策树分类模型输出图像; B: 特征筛选后输出图像。"

图7

2019年基于决策树模型的无人机图像分类结果 A: 正射图; B: 模型分类二值图。"

图8

水稻冠层覆盖度计算值与实测值相关分析及线性回归"

附图2

2020年基于决策树模型的无人机图像分类结果 (A：正射图;B：模型分类二值图)"

图9

水稻株高计算值与实测值相关分析及线性回归"

表1

2019年水稻冠层表型参数数值统计"

品种
Rice variety		 栽培品种
Cultivar		 施氮量
Nitrogen application (kg hm-2)		 估算冠层覆盖度
Estimated canopy coverage (%)		 校正后株高
Calibrated height (cm)
籼稻
Japonica		 中浙优8
Zhongzheyou 8		 0 61.76 ± 4.72 b 67.91 ± 3.44 c
150 87.30 ± 3.22 a 91.34 ± 5.37 b
300 84.86 ± 3.58 a 101.73 ± 0.45 a
Y两优900
Y Liangyou 900		 0 61.49 ± 1.29 b 58.47 ± 7.81 b
150 90.96 ± 3.61 a 78.54 ± 3.48 a
300 84.69 ± 5.47 a 87.82 ± 3.83 a
兆优5431
Zhaoyou 5431		 0 84.72 ± 5.99 b 67.41 ± 5.29 b
150 97.65 ± 0.77 a 93.08 ± 1.94 a
300 90.45 ± 2.62 ab 100.56 ± 2.95 a
粳稻
Indica		 南粳9108
Nanjing 9108		 0 54.05 ± 4.40 b 48.67 ± 2.99 b
150 90.95 ± 3.43 a 66.56 ± 2.13 a
300 90.16 ± 3.21 a 71.10 ± 1.53 a
宁粳8号
Ningjing 8		 0 39.53 ± 2.12 b 45.29 ± 8.51 b
150 70.79 ± 2.46 a 60.42 ± 1.19 a
300 68.82 ± 5.66 a 66.79 ± 1.45 a
武运粳23
Wuyunjing 23		 0 60.65 ± 7.28 b 46.79 ± 4.58 b
150 90.35 ± 3.70 a 64.13 ± 0.42 a
300 89.66 ± 3.02 a 67.89 ± 1.91 a

图10

人工标记与算法识别稻穗计数结果对比"

图11

自动识别稻穗结果 A: 原图; B: 人工标记; C: 算法识别; D: 人工标记与算法识别差别。粉色代表由算法识别后的穗, 蓝色代表人工标记的穗。"

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