基于BP神经网络的一季稻发育期预测模型

doi:10.3969/j.issn.1004-1524.2023.02.21

浙江农业学报 ›› 2023, Vol. 35 ›› Issue (2): 434-444.DOI: 10.3969/j.issn.1004-1524.2023.02.21

基于BP神经网络的一季稻发育期预测模型

樊闯(), 赵子皓, 张雪松(), 杨沈斌

南京信息工程大学应用气象学院,江苏省农业气象重点实验室,气象灾害预报预警与评估协同创新中心,江苏南京 210044

收稿日期:2022-04-19 出版日期:2023-02-25 发布日期:2023-03-14
通讯作者: 张雪松
作者简介:*张雪松,E-mail: 672508125@qq.com
樊闯(2002—),男,河北衡水人,本科生,研究方向为农业气象。E-mail: 331942074@qq.com
基金资助:
国家自然科学基金(41875140)

Prediction model of one season rice development period based on BP neural network

FAN Chuang(), ZHAO Zihao, ZHANG Xuesong(), YANG Shenbin

Jiangsu Key Laboratory of Agricultural Meteorology, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received:2022-04-19 Online:2023-02-25 Published:2023-03-14
Contact: ZHANG Xuesong

摘要/Abstract

摘要：

为探究基于BP神经网络原理开展作物发育期预测的适用性,利用长江中下游地区一季稻多年常规气象观测和农作物发育期观测资料,开展模拟研究。结果表明,只考虑温度的有效积温模型,各物候期模拟值与观测值相关性较好,相关系数(r)在0.75以上,但模拟的绝对误差较大,移栽、拔节、成熟期模拟的平均绝对误差(MAE)超过5 d。进一步以有效积温模型为基础,分别引入降水量、相对湿度、日照时数,构建温度(T)模型、温度-降水(T-P)模型、温度-相对湿度(T-RH)模型和温度-日照时数(T-S)模型,经过BP神经网络训练后,4种模型在农作物不同发育阶段的模拟评价指标均得到明显改善,并以T-RH模型最优。对中间层节点数和训练次数2项参数进行优化后的T-RH模型,对各发育阶段模拟结果的均方根误差为0.3~1.2 d,MAE小于1 d,r值均超过0.96,且达到极显著(P<0.01)水平。

关键词: 水稻, 发育期, BP神经网络, 有效积温, 相对湿度

Abstract:

In order to explore the applicability of predicting crop growth period based on the principle of BP neural network, a simulation study was carried out by using the conventional meteorological observation data and crop growth period observation data of one-season rice in the middle and lower reaches of the Yangtze River. The results showed that the effective accumulated temperature model considering temperature had a good correlation between the simulated and observed values in each phenological period with correlation coefficient (r) higher than 0.75. It was feasible to use effective accumulated temperature to simulate the crop development stage, but the mean absolute error (MAE) of simulation was large, as the MAE was more than 5 days in transplanting, jointing and maturity stages. Based on the effective accumulated temperature model, precipitation, relative humidity and sunshine duration were introduced to construct temperature (T) model, temperature-precipitation (T-P) model, temperature-relative humidity (T-RH) model and temperature-sunshine duration (T-S) model. After training by BP neural network, the evaluation indices of simulation in different crop growth periods of the four models were improved, and T-RH model exhibited the best effect. After optimization of the parameters of middle layer and training times of the T-RH model by BP neural network, the root mean square error in simulation for each growth stage was 0.3-1.2 d, the MAE was less than 1 d, and the r value was higher than 0.96 (P<0.01).

Key words: rice, growth period, BP neural network, effective accumulated temperature, relative humidity

中图分类号:

TP391.9
S511

樊闯, 赵子皓, 张雪松, 杨沈斌. 基于BP神经网络的一季稻发育期预测模型[J]. 浙江农业学报, 2023, 35(2): 434-444.

FAN Chuang, ZHAO Zihao, ZHANG Xuesong, YANG Shenbin. Prediction model of one season rice development period based on BP neural network[J]. Acta Agriculturae Zhejiangensis, 2023, 35(2): 434-444.

图/表 7

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物候期 Phenological period	RMSE/d	r	MAE/d
出苗Emergence	2.2	0.99^**	1.6
移栽Transplanting	8.8	0.77^**	6.4
拔节Jointing	6.6	0.83^**	5.1
抽穗Heading	5.5	0.89^**	4.2
成熟Mature	6.7	0.90^**	5.1

物候期 Phenological period	RMSE/d	r	MAE/d
出苗Emergence	2.2	0.99^**	1.6
移栽Transplanting	8.8	0.77^**	6.4
拔节Jointing	6.6	0.83^**	5.1
抽穗Heading	5.5	0.89^**	4.2
成熟Mature	6.7	0.90^**	5.1

评价指标 Evaluation indicator	发育阶段 Developmental stage	T模型 T model		T-P模型 T-P model		T-S模型 T-S model		T-RH模型 T-RH model
评价指标 Evaluation indicator	发育阶段 Developmental stage	训练集 Training set	测试集 Test set	训练集 Training set	测试集 Test set	训练集 Training set	测试集 Test set	训练集 Training set	测试集 Test set
RMSE/d	播种-出苗	0.6	0.7	0.6	0.7	0.6	0.6	0.3	0.3
	Sowing-emergence
	出苗-移栽	2.5	1.8	2.3	2.1	2.2	1.8	1.1	1.2
	Emergence-transplanting
	移栽-拔节	2.1	2.4	2.0	2.2	1.9	2.1	0.9	1.2
	Transplanting-jointing
	拔节-抽穗	1.7	1.5	1.6	1.3	1.5	1.3	0.6	0.7
	Jointing-heading
	抽穗-成熟	1.9	1.6	1.7	1.4	1.7	1.9	0.9	0.8
	Heading-mature
r	播种-出苗	0.93^**	0.89^**	0.94^**	0.90^**	0.94^**	0.91^**	0.96^**	0.98^**
	Sowing-emergence
	出苗-移栽	0.91^**	0.96^**	0.93^**	0.95^**	0.93^**	0.96^**	0.97^**	0.99^**
	Emergence-transplanting
	移栽-拔节	0.86^**	0.92^**	0.88^**	0.93^**	0.89^**	0.95^**	0.98^**	0.97^**
	Transplanting-jointing
	拔节-抽穗	0.92^**	0.95^**	0.94^**	0.96^**	0.94^**	0.97^**	0.96^**	0.99^**
	Jointing-heading
	抽穗-成熟	0.95^**	0.96^**	0.97^**	0.97^**	0.96^**	0.95^**	0.98^**	0.98^**
	Heading-mature
MAE/d	播种-出苗	0.5	0.6	0.5	0.5	0.4	0.4	0.2	0.3
	Sowing-emergence
	出苗-移栽	1.9	1.6	1.8	1.9	1.7	1.5	0.8	1.1
	Emergence-transplanting
	移栽-拔节	1.6	2.0	1.5	1.9	1.4	1.7	0.7	1.1
	Transplanting-jointing
	拔节-抽穗	1.4	1.2	1.2	1.0	1.2	1.0	0.5	0.7
	Jointing-heading
	抽穗-成熟	1.5	1.3	1.3	0.9	1.3	1.5	0.7	0.6
	Heading-mature

评价指标 Evaluation indicator	发育阶段 Developmental stage	T模型 T model		T-P模型 T-P model		T-S模型 T-S model		T-RH模型 T-RH model
评价指标 Evaluation indicator	发育阶段 Developmental stage	训练集 Training set	测试集 Test set	训练集 Training set	测试集 Test set	训练集 Training set	测试集 Test set	训练集 Training set	测试集 Test set
RMSE/d	播种-出苗	0.6	0.7	0.6	0.7	0.6	0.6	0.3	0.3
	Sowing-emergence
	出苗-移栽	2.5	1.8	2.3	2.1	2.2	1.8	1.1	1.2
	Emergence-transplanting
	移栽-拔节	2.1	2.4	2.0	2.2	1.9	2.1	0.9	1.2
	Transplanting-jointing
	拔节-抽穗	1.7	1.5	1.6	1.3	1.5	1.3	0.6	0.7
	Jointing-heading
	抽穗-成熟	1.9	1.6	1.7	1.4	1.7	1.9	0.9	0.8
	Heading-mature
r	播种-出苗	0.93^**	0.89^**	0.94^**	0.90^**	0.94^**	0.91^**	0.96^**	0.98^**
	Sowing-emergence
	出苗-移栽	0.91^**	0.96^**	0.93^**	0.95^**	0.93^**	0.96^**	0.97^**	0.99^**
	Emergence-transplanting
	移栽-拔节	0.86^**	0.92^**	0.88^**	0.93^**	0.89^**	0.95^**	0.98^**	0.97^**
	Transplanting-jointing
	拔节-抽穗	0.92^**	0.95^**	0.94^**	0.96^**	0.94^**	0.97^**	0.96^**	0.99^**
	Jointing-heading
	抽穗-成熟	0.95^**	0.96^**	0.97^**	0.97^**	0.96^**	0.95^**	0.98^**	0.98^**
	Heading-mature
MAE/d	播种-出苗	0.5	0.6	0.5	0.5	0.4	0.4	0.2	0.3
	Sowing-emergence
	出苗-移栽	1.9	1.6	1.8	1.9	1.7	1.5	0.8	1.1
	Emergence-transplanting
	移栽-拔节	1.6	2.0	1.5	1.9	1.4	1.7	0.7	1.1
	Transplanting-jointing
	拔节-抽穗	1.4	1.2	1.2	1.0	1.2	1.0	0.5	0.7
	Jointing-heading
	抽穗-成熟	1.5	1.3	1.3	0.9	1.3	1.5	0.7	0.6
	Heading-mature

发育阶段 Development stage	实际平均天数 Actual average days/d	RMSE/d	r	MAE/d
播种-出苗	6	0.3	0.97^**	0.2
Sowing-emergence
出苗-移栽	33	1.2	0.99^**	0.9
Emergence-transplanting
移栽-拔节	43	1.2	0.98^**	0.9
Transplanting-jointing
拔节-抽穗	28	0.7	0.99^**	0.5
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抽穗-成熟	37	0.8	0.99^**	0.6
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基于BP神经网络的一季稻发育期预测模型

Prediction model of one season rice development period based on BP neural network

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