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

doi:10.3969/j.issn.1004-1524.2023.02.21

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (2): 434-444.DOI: 10.3969/j.issn.1004-1524.2023.02.21

• Biosystems Engineering • Previous Articles Next Articles

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

Abstract

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

CLC Number:

TP391.9
S511

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.

Figures/Tables 7

References 37

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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
Jointing-heading
抽穗-成熟	37	0.8	0.99^**	0.6
Heading-mature

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

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