Comparison of simulation accuracy of leaf age models for horticultural crops driven by light and temperature factors

doi:10.3969/j.issn.1004-1524.20231311

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (6): 1368-1378.DOI: 10.3969/j.issn.1004-1524.20231311

• Biosystems Engineering • Previous Articles Next Articles

Comparison of simulation accuracy of leaf age models for horticultural crops driven by light and temperature factors

CHENG Chen¹^,²(), DONG Chaoyang³, ZHENG Shenghong⁴, ZHOU Yubo¹, ZHONG Ning¹, LI Wenming⁵, ZHU Yangchun¹, DING Fenghua¹, FENG Liping², LI Zhenfa³^,^*()

1. College of Ecology, Lishui University, Lishui 323000, Zhejiang, China
2. College of Resources and Environment Sciences, China Agricultural University, Beijing 100193, China
3. Tianjin Climate Centre, Tianjin 300074, China
4. Tea Research Institute, Lishui Academy of Agricultural and Forestry Sciences, Lishui 323000, Zhejiang, China
5. Lishui Meteorological Bureau, Lishui 323050, Zhejiang, China

Received:2023-11-20 Online:2024-06-25 Published:2024-07-02

Abstract

Abstract:

The purpose of this study was to improve the simulation accuracy of a universal leaf age model driven by light-temperature factors for horticultural crops. In order to achieve this, cucumber, celery, spinach, coriander, tulip, and tea were selected as experimental materials and 7 years (2016-2022) staged sowing experiment was conducted. Based on the relationship between crop growth and key weather factors (radiation and temperature), 4 modeling methods (accumulated temperature difference method, accumulated temperature method, physiological development time method, and accumulated product of thermal effectiveness and photosynthetically active radiation method), 6 approaches (mean value, mean of extreme values, median, stepwise regression, BP neural network, and Elman neural network) and 2 integration logics (direct and stepwise) were employed to integrate the simulation results, aiming to optimize the accuracy of the model used to construct the leaf age simulation model for horticultural crops. Results showed that: 1) The models under both integration logics exhibited high simulation accuracy, with the stepwise integration logic performing better than the direct integration logic. The differences in mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), and normalized root mean square error (NRMSE) were 0.31 d, 0.33%, 0.40 d, and 0.46% respectively. 2) The optimal time scale for the models under both integration logics was hourly, while tea was the optimal crop type, and the Elman neural network integration simulation model was the optimal modeling method. The findings of this study can provide theoretical basis and technical support for intelligent production management and visualization of horticultural crops.

Key words: horticultural crops, leaf age model, stepwise regression, neural networks, algorithmic integration logic

CLC Number:

CHENG Chen, DONG Chaoyang, ZHENG Shenghong, ZHOU Yubo, ZHONG Ning, LI Wenming, ZHU Yangchun, DING Fenghua, FENG Liping, LI Zhenfa. Comparison of simulation accuracy of leaf age models for horticultural crops driven by light and temperature factors[J]. Acta Agriculturae Zhejiangensis, 2024, 36(6): 1368-1378.

Figures/Tables 6

Table 1 Crop varieties in experiment

作物类型 Crop type	试验地点 Experiment position	品种 Variety	试验起始日期 Experiment start date	试验结束日期 Experiment end date	辐射数据 Radiation data
黄瓜Cucumber	武清区Wuqing District	津盛206 Jinsheng 206(JS)	2018-09-20	2019-02-28	√
			2018-10-17^*	2019-02-28	√
			2018-11-11	2019-02-28	√
			2019-03-10	2019-07-24	√
			2019-03-26^*	2019-07-24	√
			2019-04-10	2019-07-24	√
			2019-09-20	2020-03-28	√
			2019-10-10^*	2020-04-05	√
			2019-11-11	2020-04-19	√
郁金香Tulip	顺义区杨镇	粉色印象Fenseyinxiang(PI)	2016-12-19	2017-03-17	×
	Yang Town, Shunyi District		2016-12-29^*	2017-03-21	×
			2017-01-08	2017-03-28	×
		白日梦Bairimeng(D)	2016-12-19	2017-03-20	×
			2016-12-29^*	2017-03-24	×
			2017-01-08	2017-03-29	×
		艾斯米Aisimi(E)	2016-12-19	2017-03-25	×
			2016-12-29^*	2017-03-31	×
			2017-01-08	2017-04-06	×
		夜皇后Yehuanghou(QN)	2016-12-19	2017-04-01	×
			2016-12-29^*	2017-04-08	×
			2017-01-08	2017-04-15	×
芹菜Celery	武清区Wuqing Distrcit	尤文图斯Youwentusi(J)	2018-09-10	2019-03-14	√
			2018-10-10^*	2019-03-14	√
			2019-09-10	2020-04-14	√
			2019-09-24^*	2020-04-14	√
			2019-10-09	2020-04-14	√
	丽水学院Lishui University	尤文图斯Youwentusi(J)	2021-09-10	2022-05-13	×
			2021-10-08^*	2022-05-13	×
			2021-11-09	2022-05-13	×
菠菜Spinach	丽水学院Lishui University	大叶菠菜Dayebocai(DY)	2021-09-27	2022-03-19	×
			2021-11-23^*	2022-03-19	×
香芹Parsley	丽水学院Lishui University	四季香芹Sijixiangqin(SJ)	2021-09-27	2022-03-19	×
			2021-10-26^*	2022-03-19	×
茶Tea	松阳县Songyang County	中黄2号Zhonghuang 2(G1)、龙井Longjing(G2)、早奶白 Zaonaibai(W1)、安吉Anji (W2)、黄金甲Huangjinjia (Y1)、黄金玉Huangjinyu(Y2)	2021-12-08(斋坛乡 Zhaitan Village)^* 2021-12-08 (黄埠头村 Huangbutou Village)	——	×

Table 1 Crop varieties in experiment

作物类型 Crop type	试验地点 Experiment position	品种 Variety	试验起始日期 Experiment start date	试验结束日期 Experiment end date	辐射数据 Radiation data
黄瓜Cucumber	武清区Wuqing District	津盛206 Jinsheng 206(JS)	2018-09-20	2019-02-28	√
			2018-10-17^*	2019-02-28	√
			2018-11-11	2019-02-28	√
			2019-03-10	2019-07-24	√
			2019-03-26^*	2019-07-24	√
			2019-04-10	2019-07-24	√
			2019-09-20	2020-03-28	√
			2019-10-10^*	2020-04-05	√
			2019-11-11	2020-04-19	√
郁金香Tulip	顺义区杨镇	粉色印象Fenseyinxiang(PI)	2016-12-19	2017-03-17	×
	Yang Town, Shunyi District		2016-12-29^*	2017-03-21	×
			2017-01-08	2017-03-28	×
		白日梦Bairimeng(D)	2016-12-19	2017-03-20	×
			2016-12-29^*	2017-03-24	×
			2017-01-08	2017-03-29	×
		艾斯米Aisimi(E)	2016-12-19	2017-03-25	×
			2016-12-29^*	2017-03-31	×
			2017-01-08	2017-04-06	×
		夜皇后Yehuanghou(QN)	2016-12-19	2017-04-01	×
			2016-12-29^*	2017-04-08	×
			2017-01-08	2017-04-15	×
芹菜Celery	武清区Wuqing Distrcit	尤文图斯Youwentusi(J)	2018-09-10	2019-03-14	√
			2018-10-10^*	2019-03-14	√
			2019-09-10	2020-04-14	√
			2019-09-24^*	2020-04-14	√
			2019-10-09	2020-04-14	√
	丽水学院Lishui University	尤文图斯Youwentusi(J)	2021-09-10	2022-05-13	×
			2021-10-08^*	2022-05-13	×
			2021-11-09	2022-05-13	×
菠菜Spinach	丽水学院Lishui University	大叶菠菜Dayebocai(DY)	2021-09-27	2022-03-19	×
			2021-11-23^*	2022-03-19	×
香芹Parsley	丽水学院Lishui University	四季香芹Sijixiangqin(SJ)	2021-09-27	2022-03-19	×
			2021-10-26^*	2022-03-19	×
茶Tea	松阳县Songyang County	中黄2号Zhonghuang 2(G1)、龙井Longjing(G2)、早奶白 Zaonaibai(W1)、安吉Anji (W2)、黄金甲Huangjinjia (Y1)、黄金玉Huangjinyu(Y2)	2021-12-08(斋坛乡 Zhaitan Village)^* 2021-12-08 (黄埠头村 Huangbutou Village)	——	×

Table 2 Integrated parameters of leaf age simulation model

方法 Methods	直接集成逻辑a_NDirect integration logic				方法 Methods	分步集成逻辑 ${a_{N_}}_{sub}$ Stepwise integration logic
	不含辐热积方法 Excluding M_TEP		含辐热积方法 Including M_TEP			不含辐热积方法 Excluding M_TEP		含辐热积方法 Including M_TEP
	逐日Daily	逐时Hourly	逐日Daily	逐时Hourly		逐日Daily	逐时Hourly	逐日Daily	逐时Hourly
M_MM	—	0.380 6	-0.422 0	—	M_TD	—	0.309 8	-0.077 9	-0.266 9
M_AAA	—	—	—	—	M_AT	0.494 6	0.186 9	—	0.563 4
M_EAA	—	—	—	—	M_PDT	0.517 9	0.505 8	0.963 1	0.483 4
M_{PDT_1}	0.278 5	0.515 8	1.260 4	—	M_TEP			0.093 2	0.207 1
M_{PDT_2}	0.411 3	0.197 6	-0.805 5	1.940 7	${c_{N_}}_{sub}$	-0.252 0	0.315 5	2.544 4	2.066 3
M_{TEP_1}			0.486 6	-0.204 0	R²	0.971 7	0.988 6	0.934 2	0.943 3
M_{TEP_2}			-0.570 4	—
M_AAL	0.304 0	—	—	-0.651 3
M_EAL	—	0.114 5	10.071 9	—
M_{PDT_3}	0.918 4	—	—	—
M_{PDT_4}	-1.623 6	—	—	-2.034 5
M_{TEP_3}			—	0.197 1
M_{TEP_4}			—	—
M_ABA	-2.115 2	—	—	0.899 1
M_EBA	0.222 0	0.937 1	—	—
M_ACA	3.481 1	—	—	—
M_ECA	—	—	—	0.363 2
M_ABL	3.887 5	-0.440 1	5.975 2	1.568 9
M_EBL	-1.141 1	-0.281 9	-4.662 9	-1.096 6
M_ACL	-3.591 8	—	-4.930 6	—
M_ECL	—	-0.415 3	-5.533 8	—
M_DN	—		0.150 4
c_N	-0.593 3	0.926 0	-1.974 8	1.300 5
R²	0.981 8	0.995 3	0.967 4	0.967 4

Table 2 Integrated parameters of leaf age simulation model

方法 Methods	直接集成逻辑a_NDirect integration logic				方法 Methods	分步集成逻辑 ${a_{N_}}_{sub}$ Stepwise integration logic
	不含辐热积方法 Excluding M_TEP		含辐热积方法 Including M_TEP			不含辐热积方法 Excluding M_TEP		含辐热积方法 Including M_TEP
	逐日Daily	逐时Hourly	逐日Daily	逐时Hourly		逐日Daily	逐时Hourly	逐日Daily	逐时Hourly
M_MM	—	0.380 6	-0.422 0	—	M_TD	—	0.309 8	-0.077 9	-0.266 9
M_AAA	—	—	—	—	M_AT	0.494 6	0.186 9	—	0.563 4
M_EAA	—	—	—	—	M_PDT	0.517 9	0.505 8	0.963 1	0.483 4
M_{PDT_1}	0.278 5	0.515 8	1.260 4	—	M_TEP			0.093 2	0.207 1
M_{PDT_2}	0.411 3	0.197 6	-0.805 5	1.940 7	${c_{N_}}_{sub}$	-0.252 0	0.315 5	2.544 4	2.066 3
M_{TEP_1}			0.486 6	-0.204 0	R²	0.971 7	0.988 6	0.934 2	0.943 3
M_{TEP_2}			-0.570 4	—
M_AAL	0.304 0	—	—	-0.651 3
M_EAL	—	0.114 5	10.071 9	—
M_{PDT_3}	0.918 4	—	—	—
M_{PDT_4}	-1.623 6	—	—	-2.034 5
M_{TEP_3}			—	0.197 1
M_{TEP_4}			—	—
M_ABA	-2.115 2	—	—	0.899 1
M_EBA	0.222 0	0.937 1	—	—
M_ACA	3.481 1	—	—	—
M_ECA	—	—	—	0.363 2
M_ABL	3.887 5	-0.440 1	5.975 2	1.568 9
M_EBL	-1.141 1	-0.281 9	-4.662 9	-1.096 6
M_ACL	-3.591 8	—	-4.930 6	—
M_ECL	—	-0.415 3	-5.533 8	—
M_DN	—		0.150 4
c_N	-0.593 3	0.926 0	-1.974 8	1.300 5
R²	0.981 8	0.995 3	0.967 4	0.967 4

Fig.1 Validation of leaf age simulation model under direct integration logic a, Time scale; b, Crop type; c, Integration methods.For ease of observation, treatments with the highest simulation accuracy of the leaf age model are labelled in red, with very high simulation accuracy are labelled in green, with high simulation accuracy are labelled in blue, and the rest of the simulation accuracy levels are labelled in black. The same as below.

Table 3 Validation statistics of leaf age simulation model under direct integration logic

对象类型Object type		${\bar{X}}_{o b s}$ ±SD/d	${\bar{X}}_{s i m}$ ±SD/d	N	α	β	R²	RMSE/d	NRMSE/%	MAE/d	MRE/%
时间尺度	逐日尺度Daily scale	86.87±67.62	87.70±67.21	900	0.99	-0.34	0.98	10.37	11.93	6.68	13.96
Time scale	逐时尺度Hourly scale	86.87±67.62	85.90±66.72	900	1.00	0.58	0.98	9.01	10.37	5.59	10.43
作物类型	菠菜Spinach	85.00±38.79	87.16±36.52	168	1.03	-4.88	0.94	9.54	11.23	6.66	7.84
Crop type	茶Tea	222.44±59.59	219.95±61.30	216	0.95	12.56	0.96	11.92	5.36	8.93	4.60
	黄瓜Cucumber	60.66±32.80	61.18±33.63	600	0.94	2.98	0.93	8.60	14.18	5.03	10.51
	芹菜Celery	78.74±36.86	79.64±37.15	456	0.95	2.87	0.92	10.48	13.31	7.18	10.77
	香芹Parsley	108.81±40.46	105.49±38.60	192	1.00	3.04	0.92	12.22	11.23	7.79	8.19
	郁金香Tulip	5.07±3.84	4.85±3.45	168	0.99	0.28	0.79	1.78	35.12	1.24	40.75
集成方法	${M_{I_}}_{dirave}$	86.87±67.70	86.58±66.71	300	1.00	-0.02	0.98	10.01	11.52	7.47	10.99
Integration	${M_{I_}}_{dirmax}$	86.87±67.70	87.06±65.37	300	1.01	-0.67	0.94	16.14	18.58	10.94	16.46
methods	${M_{I_}}_{dirmed}$	86.87±67.70	86.37±67.77	300	0.99	1.55	0.98	10.13	11.66	7.59	11.79
	${M_{I_}}_{dirstep}$	86.87±67.70	86.42±67.43	300	1.00	0.57	0.99	7.02	8.08	4.94	11.20
	${M_{I_}}_{dirbp}$	86.87±67.70	87.47±67.33	300	1.00	-0.57	0.99	7.23	8.32	5.27	16.33
	${M_{I_}}_{direlman}$	86.87±67.70	86.91±67.63	300	1.00	-0.11	1.00	1.00	1.16	0.60	6.38

Table 3 Validation statistics of leaf age simulation model under direct integration logic

对象类型Object type		${\bar{X}}_{o b s}$ ±SD/d	${\bar{X}}_{s i m}$ ±SD/d	N	α	β	R²	RMSE/d	NRMSE/%	MAE/d	MRE/%
时间尺度	逐日尺度Daily scale	86.87±67.62	87.70±67.21	900	0.99	-0.34	0.98	10.37	11.93	6.68	13.96
Time scale	逐时尺度Hourly scale	86.87±67.62	85.90±66.72	900	1.00	0.58	0.98	9.01	10.37	5.59	10.43
作物类型	菠菜Spinach	85.00±38.79	87.16±36.52	168	1.03	-4.88	0.94	9.54	11.23	6.66	7.84
Crop type	茶Tea	222.44±59.59	219.95±61.30	216	0.95	12.56	0.96	11.92	5.36	8.93	4.60
	黄瓜Cucumber	60.66±32.80	61.18±33.63	600	0.94	2.98	0.93	8.60	14.18	5.03	10.51
	芹菜Celery	78.74±36.86	79.64±37.15	456	0.95	2.87	0.92	10.48	13.31	7.18	10.77
	香芹Parsley	108.81±40.46	105.49±38.60	192	1.00	3.04	0.92	12.22	11.23	7.79	8.19
	郁金香Tulip	5.07±3.84	4.85±3.45	168	0.99	0.28	0.79	1.78	35.12	1.24	40.75
集成方法	${M_{I_}}_{dirave}$	86.87±67.70	86.58±66.71	300	1.00	-0.02	0.98	10.01	11.52	7.47	10.99
Integration	${M_{I_}}_{dirmax}$	86.87±67.70	87.06±65.37	300	1.01	-0.67	0.94	16.14	18.58	10.94	16.46
methods	${M_{I_}}_{dirmed}$	86.87±67.70	86.37±67.77	300	0.99	1.55	0.98	10.13	11.66	7.59	11.79
	${M_{I_}}_{dirstep}$	86.87±67.70	86.42±67.43	300	1.00	0.57	0.99	7.02	8.08	4.94	11.20
	${M_{I_}}_{dirbp}$	86.87±67.70	87.47±67.33	300	1.00	-0.57	0.99	7.23	8.32	5.27	16.33
	${M_{I_}}_{direlman}$	86.87±67.70	86.91±67.63	300	1.00	-0.11	1.00	1.00	1.16	0.60	6.38

Fig.2 Validation of leaf age simulation model under stepwise integration logic

Table 4 Validation statistics of leaf age simulation model under stepwise integration logic

对象类型Object type		${\bar{X}}_{o b s}$ ±SD/d	${\bar{X}}_{s i m}$ ±SD/d	N	α	β	R²	RMSE/d	NRMSE/%	MAE/d	MRE/%
时间尺度	逐日尺度Daily scale	86.87±67.62	87.38±66.83	900	1.00	-0.41	0.97	10.81	12.45	7.33	13.81
Time scale	逐时尺度Hourly scale	86.87±67.62	86.27±67.19	900	1.00	0.58	0.99	7.51	8.65	5.57	9.92
作物类型	菠菜Spinach	85.00±38.79	86.78±35.93	168	1.05	-6.46	0.95	8.77	10.31	6.67	7.16
Crop type	茶Tea	222.44±59.59	219.28±61.98	216	0.94	15.74	0.96	12.59	5.66	9.80	4.98
	黄瓜Cucumber	60.66±32.80	60.56±33.32	600	0.95	3.40	0.92	9.30	15.33	6.00	12.38
	芹菜Celery	78.74±36.86	80.51±38.17	456	0.93	3.61	0.93	9.96	12.65	7.39	10.73
	香芹Parsley	108.81±40.46	106.43±38.68	192	1.03	-0.71	0.97	7.73	7.10	6.51	6.95
	郁金香Tulip	5.07±3.84	5.12±3.25	168	1.12	-0.65	0.90	1.27	25.02	0.90	32.28
集成方法	${M_{I_}}_{subave}$	86.87±67.70	86.89±67.00	300	1.00	0.19	0.97	10.75	12.37	7.47	11.16
Integration	${M_{I_}}_{submax}$	86.87±67.70	87.12±66.79	300	1.00	-0.04	0.97	11.93	13.73	8.22	12.49
methods	${M_{I_}}_{submed}$	86.87±67.70	86.69±67.54	300	0.99	0.99	0.98	10.25	11.80	7.31	11.19
	${M_{I_}}_{substep}$	86.87±67.70	86.60±67.15	300	1.00	0.23	0.99	8.25	9.50	6.19	10.45
	${M_{I_}}_{subbp}$	86.87±67.70	86.67±66.77	300	1.01	-0.35	0.99	8.19	9.43	6.25	12.18
	${M_{I_}}_{subel m a n}$	86.87±67.70	87.00±67.25	300	1.00	-0.49	1.00	4.70	5.41	3.25	13.72

Table 4 Validation statistics of leaf age simulation model under stepwise integration logic

对象类型Object type		${\bar{X}}_{o b s}$ ±SD/d	${\bar{X}}_{s i m}$ ±SD/d	N	α	β	R²	RMSE/d	NRMSE/%	MAE/d	MRE/%
时间尺度	逐日尺度Daily scale	86.87±67.62	87.38±66.83	900	1.00	-0.41	0.97	10.81	12.45	7.33	13.81
Time scale	逐时尺度Hourly scale	86.87±67.62	86.27±67.19	900	1.00	0.58	0.99	7.51	8.65	5.57	9.92
作物类型	菠菜Spinach	85.00±38.79	86.78±35.93	168	1.05	-6.46	0.95	8.77	10.31	6.67	7.16
Crop type	茶Tea	222.44±59.59	219.28±61.98	216	0.94	15.74	0.96	12.59	5.66	9.80	4.98
	黄瓜Cucumber	60.66±32.80	60.56±33.32	600	0.95	3.40	0.92	9.30	15.33	6.00	12.38
	芹菜Celery	78.74±36.86	80.51±38.17	456	0.93	3.61	0.93	9.96	12.65	7.39	10.73
	香芹Parsley	108.81±40.46	106.43±38.68	192	1.03	-0.71	0.97	7.73	7.10	6.51	6.95
	郁金香Tulip	5.07±3.84	5.12±3.25	168	1.12	-0.65	0.90	1.27	25.02	0.90	32.28
集成方法	${M_{I_}}_{subave}$	86.87±67.70	86.89±67.00	300	1.00	0.19	0.97	10.75	12.37	7.47	11.16
Integration	${M_{I_}}_{submax}$	86.87±67.70	87.12±66.79	300	1.00	-0.04	0.97	11.93	13.73	8.22	12.49
methods	${M_{I_}}_{submed}$	86.87±67.70	86.69±67.54	300	0.99	0.99	0.98	10.25	11.80	7.31	11.19
	${M_{I_}}_{substep}$	86.87±67.70	86.60±67.15	300	1.00	0.23	0.99	8.25	9.50	6.19	10.45
	${M_{I_}}_{subbp}$	86.87±67.70	86.67±66.77	300	1.01	-0.35	0.99	8.19	9.43	6.25	12.18
	${M_{I_}}_{subel m a n}$	86.87±67.70	87.00±67.25	300	1.00	-0.49	1.00	4.70	5.41	3.25	13.72

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Comparison of simulation accuracy of leaf age models for horticultural crops driven by light and temperature factors

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