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${{a}_{N\_}}_{\text{sub}}$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	${{c}_{N\_}}_{\text{sub}}$	-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		$X - o b s$ ±SD/d	$X - s i m$ ±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	${{M}_{I\_}}_{\text{dirave}}$	86.87±67.70	86.58±66.71	300	1.00	-0.02	0.98	10.01	11.52	7.47	10.99
Integration	${{M}_{I\_}}_{\text{dirmax}}$	86.87±67.70	87.06±65.37	300	1.01	-0.67	0.94	16.14	18.58	10.94	16.46
methods	${{M}_{I\_}}_{\text{dirmed}}$	86.87±67.70	86.37±67.77	300	0.99	1.55	0.98	10.13	11.66	7.59	11.79
	${{M}_{I\_}}_{\text{dirstep}}$	86.87±67.70	86.42±67.43	300	1.00	0.57	0.99	7.02	8.08	4.94	11.20
	${{M}_{I\_}}_{\text{dirbp}}$	86.87±67.70	87.47±67.33	300	1.00	-0.57	0.99	7.23	8.32	5.27	16.33
	${{M}_{I\_}}_{\text{direlman}}$	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		$X - o b s$ ±SD/d	$X - s i m$ ±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	${{M}_{I\_}}_{\text{dirave}}$	86.87±67.70	86.58±66.71	300	1.00	-0.02	0.98	10.01	11.52	7.47	10.99
Integration	${{M}_{I\_}}_{\text{dirmax}}$	86.87±67.70	87.06±65.37	300	1.01	-0.67	0.94	16.14	18.58	10.94	16.46
methods	${{M}_{I\_}}_{\text{dirmed}}$	86.87±67.70	86.37±67.77	300	0.99	1.55	0.98	10.13	11.66	7.59	11.79
	${{M}_{I\_}}_{\text{dirstep}}$	86.87±67.70	86.42±67.43	300	1.00	0.57	0.99	7.02	8.08	4.94	11.20
	${{M}_{I\_}}_{\text{dirbp}}$	86.87±67.70	87.47±67.33	300	1.00	-0.57	0.99	7.23	8.32	5.27	16.33
	${{M}_{I\_}}_{\text{direlman}}$	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		$X - o b s$ ±SD/d	$X - s i m$ ±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	${{M}_{I\_}}_{\text{subave}}$	86.87±67.70	86.89±67.00	300	1.00	0.19	0.97	10.75	12.37	7.47	11.16
Integration	${{M}_{I\_}}_{\text{submax}}$	86.87±67.70	87.12±66.79	300	1.00	-0.04	0.97	11.93	13.73	8.22	12.49
methods	${{M}_{I\_}}_{\text{submed}}$	86.87±67.70	86.69±67.54	300	0.99	0.99	0.98	10.25	11.80	7.31	11.19
	${{M}_{I\_}}_{\text{substep}}$	86.87±67.70	86.60±67.15	300	1.00	0.23	0.99	8.25	9.50	6.19	10.45
	${{M}_{I\_}}_{\text{subbp}}$	86.87±67.70	86.67±66.77	300	1.01	-0.35	0.99	8.19	9.43	6.25	12.18
	${{M}_{I\_}}_{\text{subel}man}$	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		$X - o b s$ ±SD/d	$X - s i m$ ±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	${{M}_{I\_}}_{\text{subave}}$	86.87±67.70	86.89±67.00	300	1.00	0.19	0.97	10.75	12.37	7.47	11.16
Integration	${{M}_{I\_}}_{\text{submax}}$	86.87±67.70	87.12±66.79	300	1.00	-0.04	0.97	11.93	13.73	8.22	12.49
methods	${{M}_{I\_}}_{\text{submed}}$	86.87±67.70	86.69±67.54	300	0.99	0.99	0.98	10.25	11.80	7.31	11.19
	${{M}_{I\_}}_{\text{substep}}$	86.87±67.70	86.60±67.15	300	1.00	0.23	0.99	8.25	9.50	6.19	10.45
	${{M}_{I\_}}_{\text{subbp}}$	86.87±67.70	86.67±66.77	300	1.01	-0.35	0.99	8.19	9.43	6.25	12.18
	${{M}_{I\_}}_{\text{subel}man}$	86.87±67.70	87.00±67.25	300	1.00	-0.49	1.00	4.70	5.41	3.25	13.72

References 27

[1]	PANTIN F, SIMONNEAU T, MULLER B. Coming of leaf age: control of growth by hydraulics and metabolics during leaf ontogeny[J]. The New Phytologist, 2012, 196(2): 349-366.
[2]	陈凯利, 李建明, 贺会强, 等. 水分对番茄不同叶龄叶片光合作用的影响[J]. 生态学报, 2013, 33(16): 4919-4929.
	CHEN K L, LI J M, HE H Q, et al. Effects of water on photosynthesis in different age of tomato leaves[J]. Acta Ecologica Sinica, 2013, 33(16): 4919-4929. (in Chinese with English abstract)
[3]	程陈, 冯利平, 薛庆禹, 等. 日光温室黄瓜生长发育模拟模型[J]. 应用生态学报, 2019, 30(10): 3491-3500.
	CHENG C, FENG L P, XUE Q Y, et al. Simulation model for cucumber growth and development in sunlight greenhouse[J]. Chinese Journal of Applied Ecology, 2019, 30(10): 3491-3500. (in Chinese with English abstract)
[4]	魏广彬, 徐蕊, 孙和平, 等. 叶龄模型在水稻上应用的检验与比较[J]. 江苏农业学报, 2013, 29(4): 696-707.
	WEI G B, XU R, SUN H P, et al. Validation and comparison of the precision of leaf emergence models on rice[J]. Jiangsu Journal of Agricultural Sciences, 2013, 29(4): 696-707. (in Chinese with English abstract)
[5]	DINSSA F F, YANG R Y, LEDESMA D R, et al. Effect of leaf harvest on grain yield and nutrient content of diverse amaranth entries[J]. Scientia Horticulturae, 2018, 236: 146-157.
[6]	ELLIS R H, QI A, SUMMERFIELD R J, et al. Rates of leaf appearance and panicle development in rice (Oryza sativa L.): a comparison at three temperatures[J]. Agricultural and Forest Meteorology, 1993, 66(3/4): 129-138.
[7]	姜会飞, 郭勇, 张玉莹, 等. 不同下限基点温度对积温模型模拟效果的影响[J]. 中国农业大学学报, 2018, 23(5): 131-141.
	JIANG H F, GUO Y, ZHANG Y Y, et al. Impact of base temperature on the growing degree-day and simulation effect of GDD model[J]. Journal of China Agricultural University, 2018, 23(5): 131-141. (in Chinese with English abstract)
[8]	张智优, 曹宏鑫, 陈兵林, 等. 设施番茄发育期与叶龄动态模拟模型研究[J]. 中国农业气象, 2011, 32(4): 550-557.
	ZHANG Z Y, CAO H X, CHEN B L, et al. Research on the simulation models of phenophase and leaf number of controlled tomato[J]. Chinese Journal of Agrometeorology, 2011, 32(4): 550-557. (in Chinese with English abstract)
[9]	程陈, 董朝阳, 黎贞发, 等. 日光温室芹菜外观形态及干物质积累分配模拟模型[J]. 农业工程学报, 2021, 37(10): 142-151.
	CHENG C, DONG C Y, LI Z F, et al. Simulation model of external morphology and dry matter accumulation and distribution of celery in solar greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(10): 142-151. (in Chinese with English abstract)
[10]	TREWAVAS A. A brief history of systems biology. “Every object that biology studies is a system of systems.” Francois Jacob (1974)[J]. The Plant Cell, 2006, 18(10): 2420-2430.
[11]	余卫东, 冯利平. 小时和日步长热时对夏玉米生育期模拟的影响[J]. 农业工程学报, 2021, 37(7): 131-139.
	YU W D, FENG L P. Comparison of the simulation effects of summer maize phenology derived from hourly and daily time step thermal units[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(7): 131-139. (in Chinese with English abstract)
[12]	徐立鸿, 孟凡峥, 蔚瑞华. 秒尺度温室番茄作物-环境互作模型构建与验证[J]. 农业工程学报, 2021, 37(8): 212-222.
	XU L H, MENG F Z, WEI R H. Development and verification of tomato crop-environment interaction model in second timescale greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(8): 212-222. (in Chinese with English abstract)
[13]	MUSTAFA S M T, NOSSENT J, GHYSELS G, et al. Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling[J]. Environmental Modelling & Software, 2020, 126: 104654.
[14]	ASSENG S, EWERT F, ROSENZWEIG C, et al. Uncertainty in simulating wheat yields under climate change[J]. Nature Climate Change, 2013, 3(9): 827-832.
[15]	MARTRE P, WALLACH D, ASSENG S, et al. Multimodel ensembles of wheat growth: many models are better than one[J]. Global Change Biology, 2015, 21(2): 911-925.
[16]	ROSENZWEIG C, JONES J W, HATFIELD J L, et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies[J]. Agricultural and Forest Meteorology, 2013, 170: 166-182.
[17]	ZHAO C, LIU B, XIAO L J, et al. A SIMPLE crop model[J]. European Journal of Agronomy, 2019, 104: 97-106.
[18]	程陈, 李春, 李文明, 等. 园艺作物发育期和采收期模拟模型的最优模拟路径[J]. 农业工程学报, 2023, 39(12): 158-167.
	CHENG C, LI C, LI W M, et al. Optimal path of the simulation model in horticultural crop development and harvest period[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(12): 158-167. (in Chinese with English abstract)
[19]	辛菊琴, 蒋艳, 舒少龙. 综合用户偏好模型和BP神经网络的个性化推荐[J]. 计算机工程与应用, 2013, 49(2): 57-60, 96.
	XIN J Q, JIANG Y, SHU S L. Personal recommendation algorithm with customer preference model and BP neural networks[J]. Computer Engineering and Applications, 2013, 49(2): 57-60, 96. (in Chinese with English abstract)
[20]	程陈, 冯利平, 董朝阳, 等. 利用Elman神经网络的华北棚型日光温室室内环境要素模拟[J]. 农业工程学报, 2021, 37(13): 200-208.
	CHENG C, FENG L P, DONG C Y, et al. Simulation of inside environmental factors in solar greenhouses using Elman neural network in North China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(13): 200-208. (in Chinese with English abstract)
[21]	夏克文, 李昌彪, 沈钧毅. 前向神经网络隐含层节点数的一种优化算法[J]. 计算机科学, 2005, 32(10): 143-145.
	XIA K W, LI C B, SHEN J Y. An optimization algorithm on the number of hidden layer nodes in feed-forward neural network[J]. Computer Science, 2005, 32(10): 143-145. (in Chinese with English abstract)
[22]	程陈, 黎贞发, 董朝阳, 等. 日光温室黄瓜和芹菜不同位置消光系数模拟及验证[J]. 农业工程学报, 2020, 36(21): 243-252.
	CHENG C, LI Z F, DONG C Y, et al. Simulation and validation of extinction coefficient at different positions of cucumber and celery in solar greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(21): 243-252. (in Chinese with English abstract)
[23]	CHENG C, FENG L P, BIENVENIDO BARCENA J F, et al. A growth model based on standardized growing degree days for hydroponic fresh cut tulip in solar greenhouses[J]. European Journal of Horticultural Science, 2022, 87(4): 1-13.
[24]	PURCELL L C. Comparison of thermal units derived from daily and hourly temperatures[J]. Crop Science, 2003, 43(5): 1874-1879.
[25]	丁为民, 汪小旵, 李毅念, 等. 温室环境控制与温室模拟模型研究现状分析[J]. 农业机械学报, 2009, 40(5): 162-168.
	DING W M, WANG X C, LI Y N, et al. Review on environmental control and simulation models for greenhouses[J]. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40(5): 162-168. (in Chinese with English abstract)
[26]	HADASCH S, FORKMAN J, PIEPHO H P. Cross-validation in AMMI and GGE models: a comparison of methods[J]. Crop Science, 2017, 57(1): 264-274.
[27]	ZHENG J H, ZHANG S. Improving rice phenology simulations based on the Bayesian model averaging method[J]. European Journal of Agronomy, 2023, 142: 126646.

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

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