基于低空无人机遥感的水稻产量估测方法研究进展

doi:10.16819/j.1001-7216.2024.240103

中国水稻科学 ›› 2024, Vol. 38 ›› Issue (6): 604-616.DOI: 10.16819/j.1001-7216.2024.240103

基于低空无人机遥感的水稻产量估测方法研究进展

冯向前¹^,²^,^#, 王爱冬¹^,^#, 洪卫源¹, 李子秋¹, 覃金华¹^,², 詹丽钏³, 陈里鹏⁴, 张运波², 王丹英¹^,^*(), 陈松¹^,^*()

¹中国水稻研究所水稻生物育种全国重点实验室，杭州 311401
²长江大学农学院，湖北荆州 434025
³嵊州市农业技术推广中心，浙江绍兴 312400
⁴杭州富阳栋山农机服务专业合作社，杭州 311400

收稿日期:2024-01-04 修回日期:2024-07-01 出版日期:2024-11-10 发布日期:2024-11-15
通讯作者: *email: wangdanying@caas.cn;chensong02@caas.cn
作者简介:^#共同第一作者
基金资助:
国家重点研发计划资助项目(2022YFD2300700);国家水稻产业技术体系资助项目(CARS-01);中国农业科学院科技创新工程重大科研任务资助项目(CAAS-ZDRW202001);水稻生物育种全国重点实验室自主课题(2023ZZKT20402)

Research Progress in Rice Yield Estimation Method Based on Low-altitude Unmanned Aerial Vehicle Remote Sensing

FENG Xiangqian¹^,²^,^#, WANG Aidong¹^,^#, HONG Weiyuan¹, LI Ziqiu¹, QIN Jinhua¹^,², ZHAN Lichuan³, CHEN Lipeng⁴, ZHANG Yunbo², WANG Danying¹^,^*(), CHEN Song¹^,^*()

¹State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou 311401, China
²College of Agriculture, Yangtze University, Jingzhou 434025, China
³Shengzhou Agricultural Technology Extension Center, Shaoxing 312400, China
⁴Hangzhou Fuyang Dongshan Agricultural Machinery Service Professional Cooperatives, Hangzhou 311400, China

Received:2024-01-04 Revised:2024-07-01 Online:2024-11-10 Published:2024-11-15
Contact: *email: wangdanying@caas.cn;chensong02@caas.cn
About author:^#These authors contributed equally to this work

摘要/Abstract

摘要：

水稻作为主要的粮食作物之一，其产量估测对国家政策宏观调控、地方农情实时指导以及优良品种的定向培育都起着至关重要的作用。随着作物学科及其交叉学科的不断进步，估产的方法和模式也逐渐多样化。同时，随着遥感技术的发展，尤其是低空无人机的出现及其应用的普及，水稻智能遥感估产方法不断创新，估测精度不断提升，但针对基于无人机遥感的智能稻作产量估测缺乏系统和科学的归纳总结。鉴于此，本文在梳理目前主流水稻估产方法及其优缺点的基础上，聚焦探讨低空智能遥感技术在水稻产量估测中的应用及未来发展方向。围绕当前利用低空遥感技术获取的主要特征信息，探讨实现智能遥感水稻估产的模型开发。此外，还探讨了智能遥感技术在水稻产量估测中面临的挑战和问题，旨在深化并完善对低空遥感水稻的产量估测方法的理解，进而为水稻产量智能估计提供系统全面的参考和指导。

关键词: 水稻, 产量估计, 遥感, 无人机

Abstract:

Rice is one of the primary staple crops. Its yield estimation plays a crucial role in the macro-control of national policies, real-time guidance according to local agricultural conditions, and targeted breeding of elite varieties. Therefore, estimating rice grain yield is of great significance. With the continuous advancement in crop science and interdisciplinary approaches, the methods and models for estimating rice yield have diversified greatly. Concurrently, the development of remote sensing technology, particularly the emergence and popularization of low-altitude Unmanned Aerial Vehicles (UAVs), has led to continuous improvements in intelligent remote sensing methods and their accuracy for rice yield estimation. However, there is a lack of systematic and scientific summarization of intelligent rice yield estimation, specifically utilizing UAV remote sensing. This work provides a comprehensive review of the current mainstream methodologies for estimating rice yield, critically evaluating their advantages and limitations. It then explores the application and future directions of intelligent low-altitude remote sensing technology in rice yield estimation. By analyzing the key characteristic information obtained through remote sensing, the article examines the development of primary yield estimation models. It discusses the challenges and limitations encountered when employing intelligent remote sensing for rice yield estimation. The ultimate goal is to enhance our understanding of rice yield estimation methods and provide a systematic and comprehensive reference for the development of intelligent rice yield estimation techniques.

Key words: rice, yield estimation, remote sensing, unmanned aerial vehicle

冯向前, 王爱冬, 洪卫源, 李子秋, 覃金华, 詹丽钏, 陈里鹏, 张运波, 王丹英, 陈松. 基于低空无人机遥感的水稻产量估测方法研究进展[J]. 中国水稻科学, 2024, 38(6): 604-616.

FENG Xiangqian, WANG Aidong, HONG Weiyuan, LI Ziqiu, QIN Jinhua, ZHAN Lichuan, CHEN Lipeng, ZHANG Yunbo, WANG Danying, CHEN Song. Research Progress in Rice Yield Estimation Method Based on Low-altitude Unmanned Aerial Vehicle Remote Sensing[J]. Chinese Journal OF Rice Science, 2024, 38(6): 604-616.

图/表 5

图1 WOS(web of science)上以遥感(remote sensing)、作物(crop)、产量(yield)为主题关键词的已发表论文数量(截至2023年12月31日)

Fig. 1. Number of published papers on web of science with remote sensing, crop, yield as subject keywords (Before 31 December 2023)

图2 WOS上以作物产量、植被指数为主题关键词的已发表论文数量(截至2023年12月31日)

Fig. 2. Number of published papers on web of science with crop yield and vegetation index as subject keywords (Before 31 December 2023)

表1 基于灰度共生矩阵的纹理特征

Table 1. Textures based on GLCM

纹理特征 Textures	公式 Formula	含义 Meaning
平均值Mean	$μ i = ∑ i, j = 0 N - 1 ⅈ (P i, j)$ ; $μ j = ∑ i, j = 0 N - 1 j (P i, j)$	图像的平均灰度值 Average grayscale value of an image
同质性Homogeneity	$H O M = ∑ i, j = 0 N - 1 P i, j / [1 + i - j 2]$	图像纹理局部变化的度量 Measurement of local texture variation in an image.
方差Variance	$δ i 2 = ∑ i, j = 0 N - 1 P i, j (i - μ i) 2$ ; $δ j 2 = ∑ i, j = 0 N - 1 P i, j (j - μ j) 2$	图像中灰度变化的度量 Measurement of grayscale variation in an image.
对比度Contrast	$C O N = ∑ i, j = 0 N - 1 P i, j (i - j) 2$	相邻像素之间像素值局部变化的度量 Measurement of local pixel value changes between adjacent pixels.
非相似度Dissimilarity	$D I S = ∑ i, j = 0 N - 1 P i, j ⅈ - j$	图像灰度差异的度量 Measurement of grayscale intensity differences in an image.
熵Entropy	$E N T = ∑ i, j = 0 N - 1 P i, j (- ln ⁡ P i, j)$	表征图像的紊乱程度 Characterizing the degree of disorder in an image.
相关性Correlation	$C O R = ∑ i, j = 0 N - 1 P i, j$	相邻像素之间线性度的度量 Measurement of linearity between adjacent pixels.
角二阶矩 Angular Second Moment	$A S M = ∑ i, j = 0 N - 1 P i, j 2$	又称能量，图像灰度分布均匀性的度量 Also known as energy, Measurement of the uniformity of the image’s grayscale distribution.

表1 基于灰度共生矩阵的纹理特征

Table 1. Textures based on GLCM

纹理特征 Textures	公式 Formula	含义 Meaning
平均值Mean	$μ i = ∑ i, j = 0 N - 1 ⅈ (P i, j)$ ; $μ j = ∑ i, j = 0 N - 1 j (P i, j)$	图像的平均灰度值 Average grayscale value of an image
同质性Homogeneity	$H O M = ∑ i, j = 0 N - 1 P i, j / [1 + i - j 2]$	图像纹理局部变化的度量 Measurement of local texture variation in an image.
方差Variance	$δ i 2 = ∑ i, j = 0 N - 1 P i, j (i - μ i) 2$ ; $δ j 2 = ∑ i, j = 0 N - 1 P i, j (j - μ j) 2$	图像中灰度变化的度量 Measurement of grayscale variation in an image.
对比度Contrast	$C O N = ∑ i, j = 0 N - 1 P i, j (i - j) 2$	相邻像素之间像素值局部变化的度量 Measurement of local pixel value changes between adjacent pixels.
非相似度Dissimilarity	$D I S = ∑ i, j = 0 N - 1 P i, j ⅈ - j$	图像灰度差异的度量 Measurement of grayscale intensity differences in an image.
熵Entropy	$E N T = ∑ i, j = 0 N - 1 P i, j (- ln ⁡ P i, j)$	表征图像的紊乱程度 Characterizing the degree of disorder in an image.
相关性Correlation	$C O R = ∑ i, j = 0 N - 1 P i, j$	相邻像素之间线性度的度量 Measurement of linearity between adjacent pixels.
角二阶矩 Angular Second Moment	$A S M = ∑ i, j = 0 N - 1 P i, j 2$	又称能量，图像灰度分布均匀性的度量 Also known as energy, Measurement of the uniformity of the image’s grayscale distribution.

图3 基于遥感信息、统计模型和作物模型估测水稻产量的模型框架 RS为遥感数据，AD为农艺数据。

Fig. 3. Model framework for estimating rice yield based on remote sensing information, statistical models and crop models RS is remote sensing data; AD is agronomic data.

图4 作物模型与遥感数据融合流程

Fig. 4. Crop model and remote sensing data integration process

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基于低空无人机遥感的水稻产量估测方法研究进展

Research Progress in Rice Yield Estimation Method Based on Low-altitude Unmanned Aerial Vehicle Remote Sensing

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