Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms

doi:10.3969/j.issn.1004-1524.20231368

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (12): 2812-2822.DOI: 10.3969/j.issn.1004-1524.20231368

• Biosystems Engineering • Previous Articles Next Articles

Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms

ZHANG Yongbin¹(), LI Xiang¹, MAN Weidong¹^,²^,³, LIU Mingyue¹^,²^,⁴^,^*(), FAN Jihao⁵, HU Haoran⁵, SONG Lijie¹, LIU Weijia¹

1. College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China
2. Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan 063210, Hebei, China
3. Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, Hebei, China
4. Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources, Tangshan 063210, Hebei, China
5. Hebei Tangshan High Resolution Earth Observation System Data and Application Center, Tangshan 063210, Hebei, China

Received:2023-12-06 Online:2024-12-25 Published:2024-12-27

Abstract

Abstract:

Aiming at the problem that optical images are easily affected by cloud and rain weather, resulting in low accuracy of crop yield estimation, in this study, the Sentinel-1/2 spectral information and backscattering coefficient at winter wheat heading stage were combined, and three machine learning regression methods of extreme gradient boosting, random forest and support vector machine were used to establish the winter wheat yield estimation model in Tangshan, the best model was selected to realize the winter wheat yield inversion in Tangshan. The results show that:the extreme gradient boosting model based on vegetation index and backscattering coefficient had the best estimation effect, with the determination coefficient (R²) of 0.654, the root mean square error (RMSE) of 0.499 t·hm^-2, and the normalized root mean square error (nRMSE) of 10.02%. Among the 24 remote sensing feature variables, the importance of NDMI, NDVIre3 and NDVIre2 was much higher than that of the backscattering coefficient. Inverse spatial distribution of winter wheat yield in Tangshan based on optimal yield estimation model, the yield range of winter wheat was mainly concentrated in 7.00-8.00 t·hm^-2, accounting for 40.75%, the distribution of winter wheat yield was generally similar to the ground truth. This study proposed Sentinel-1/2 data and integration of machine learning algorithms of winter wheat yield estimation method, effectively improve the inversion accuracy of winter wheat yield and machine learning method to strengthen the explanatory of the model, the method has certain feasibility.

Key words: remote sensing, yield, winter wheat, Sentinel-1/2 data, machine learning algorithm

CLC Number:

S512.11

ZHANG Yongbin, LI Xiang, MAN Weidong, LIU Mingyue, FAN Jihao, HU Haoran, SONG Lijie, LIU Weijia. Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms[J]. Acta Agriculturae Zhejiangensis, 2024, 36(12): 2812-2822.

Figures/Tables 8

References 37

[1]	KHANAL S, KUSHAL K C, FULTON J P, et al. Remote sensing in agriculture:accomplishments, limitations, and opportunities[J]. Remote Sensing, 2020, 12(22):3783.
[2]	王汇涵, 张泽, 康孝岩, 等. 基于Sentinel-2A的棉花种植面积提取及产量预测[J]. 农业工程学报, 2022, 38(9):205-214.
	WANG H H, ZHANG Z, KANG X Y, et al. Cotton planting area extraction and yield prediction based on Sentinel-2A[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(9):205-214. (in Chinese with English abstract)
[3]	王莺, 巩垠熙. 遥感光谱技术在农作物估产中的应用研究进展[J]. 中国农学通报, 2019, 35(3):69-75.
	WANG Y, GONG Y X. Spectral remote sensing technology applied in crop yield estimation:research progress[J]. Chinese Agricultural Science Bulletin, 2019, 35(3):69-75. (in Chinese with English abstract)
[4]	CLAUSS K, OTTINGER M, LEINENKUGEL P, et al. Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 73:574-585.
[5]	BATTUDE M, AL BITAR A, MORIN D, et al. Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data[J]. Remote Sensing of Environment, 2016, 184:668-681.
[6]	TAŞAN S, CEMEK B, TAŞAN M, et al. Estimation of eggplant yield with machine learning methods using spectral vegetation indices[J]. Computers and Electronics in Agriculture, 2022, 202:107367.
[7]	ZHANG J Q, TIAN H R, WANG P X, et al. Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China[J]. Computers and Electronics in Agriculture, 2022, 192:106616.
[8]	MA Y C, ZHANG Z, KANG Y H, et al. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach[J]. Remote Sensing of Environment, 2021, 259:112408.
[9]	刘红超, 梁燕, 张喜旺. 多时相影像的冬小麦种植面积提取及估产[J]. 遥感信息, 2017, 32(5):87-92.
	LIU H C, LIANG Y, ZHANG X W. Winter wheat area extraction and yield estimation using multi-temporal images[J]. Remote Sensing Information, 2017, 32(5):87-92. (in Chinese with English abstract)
[10]	WANG J J, DAI Q X, SHANG J L, et al. Field-scale rice yield estimation using sentinel-1A synthetic aperture radar (SAR) data in coastal saline region of Jiangsu province, China[J]. Remote Sensing, 2019, 11(19):2274.
[11]	王鹏新, 田惠仁, 张悦, 等. 基于深度学习的作物长势监测和产量估测研究进展[J]. 农业机械学报, 2022, 53(2):1-14.
	WANG P X, TIAN H R, ZHANG Y, et al. Crop growth monitoring and yield estimation based on deep learning:state of the art and beyond[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(2):1-14. (in Chinese with English abstract)
[12]	孙少杰, 吴门新, 庄立伟, 等. 基于CNN卷积神经网络和BP神经网络的冬小麦县级产量预测[J]. 农业工程学报, 2022, 38(11):151-160.
	SUN S J, WU M X, ZHUANG L W, et al. Forecasting winter wheat yield at county level using CNN and BP neural networks[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(11):151-160. (in Chinese with English abstract)
[13]	严海军, 卓越, 李茂娜, 等. 基于机器学习和无人机多光谱遥感的苜蓿产量预测[J]. 农业工程学报, 2022, 38(11):64-71.
	YAN H J, ZHUO Y, LI M N, et al. Alfalfa yield prediction using machine learning and UAV multispectral remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(11):64-71. (in Chinese with English abstract)
[14]	CHENG E H, ZHANG B, PENG D L, et al. Wheat yield estimation using remote sensing data based on machine learning approaches[J]. Frontiers in Plant Science, 2022, 13:1090970.
[15]	HUNT M L, BLACKBURN G A, CARRASCO L, et al. High resolution wheat yield mapping using Sentinel-2[J]. Remote Sensing of Environment, 2019, 233:111410.
[16]	王恺宁, 王修信. 多植被指数组合的冬小麦遥感估产方法研究[J]. 干旱区资源与环境, 2017, 31(7):44-49.
	WANG K N, WANG X X. Research on winter wheat yield estimation with the multiply remote sensing vegetation index combination[J]. Journal of Arid Land Resources and Environment, 2017, 31(7):44-49. (in Chinese with English abstract)
[17]	王耀民, 陈皓锐, 陈俊英, 等. 光谱指数筛选方法与统计回归算法结合的水稻估产模型对比[J]. 农业工程学报, 2021, 37(21):208-216.
	WANG Y M, CHEN H R, CHEN J Y, et al. Comparation of rice yield estimation model combining spectral index screening method and statistical regression algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(21):208-216. (in Chinese with English abstract)
[18]	郭力娜, 王奉林, 姜广辉, 等. 基于GIS的市域休闲农业园区距离分布特征:以唐山市为例[J]. 中国农业资源与区划, 2020, 41(7):248-254.
	GUO L N, WANG F L, JIANG G H, et al. Distance distribution characteristics of urban leisure agriculture parks based on GIS:a case study of Tangshan City[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2020, 41(7):248-254. (in Chinese with English abstract)
[19]	李蕊, 李国清, 卢小平, 等. 一种改进的时间序列下冬小麦遥感识别方法[J]. 测绘科学, 2022, 47(4):73-79.
	LI R, LI G Q, LU X P, et al. An improved method of winter wheat remote sensing recognition based on time series[J]. Science of Surveying and Mapping, 2022, 47(4):73-79. (in Chinese with English abstract)
[20]	李粉玲, 王力, 刘京, 等. 基于高分一号卫星数据的冬小麦叶片SPAD值遥感估算[J]. 农业机械学报, 2015, 46(9):273-281.
	LI F L, WANG L, LIU J, et al. Remote sensing estimation of SPAD value for wheat leaf based on GF-1 data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(9):273-281. (in Chinese with English abstract)
[21]	周蔚, 吕延杰, 林起楠. 联合光学和合成孔径雷达数据的太平湖森林地上生物量反演研究[J]. 西北林学院学报, 2023, 38(2):193-200.
	ZHOU W, LÜ Y J, LIN Q N. Retrieval of above ground biomass in Taiping Lake forests using optical and SAR dataset[J]. Journal of Northwest Forestry University, 2023, 38(2):193-200. (in Chinese with English abstract)
[22]	杨靖雅, 胡琼, 魏浩东, 等. 基于Sentinel-1/2数据的中国南方单双季稻识别结果一致性分析[J]. 中国农业科学, 2022, 55(16):3093-3109.
	YANG J Y, HU Q, WEI H D, et al. Consistency analysis of classification results for single and double cropping rice in Southern China based on sentinel-1/2 imagery[J]. Scientia Agricultura Sinica, 2022, 55(16):3093-3109. (in Chinese with English abstract)
[23]	HUBER F, YUSHCHENKO A, STRATMANN B, et al. Extreme gradient boosting for yield estimation compared with deep learning approaches[J]. Computers and Electronics in Agriculture, 2022, 202:107346.
[24]	LI Y C, ZENG H W, ZHANG M, et al. A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 118:103269.
[25]	王腾军, 方珂, 杨耘, 等. 随机森林回归模型用于土壤重金属含量多光谱遥感反演[J]. 测绘通报, 2021(11):92-95.
	WANG T J, FANG K, YANG Y, et al. Multi-spectral remote sensing inversion of soil heavy metal content using random forest regression model[J]. Bulletin of Surveying and Mapping, 2021(11):92-95. (in Chinese with English abstract)
[26]	兰铭, 费帅鹏, 禹小龙, 等. 多光谱与热红外数据融合在冬小麦产量估测中的应用[J]. 麦类作物学报, 2021, 41(12):1564-1572.
	LAN M, FEI S P, YU X L, et al. Application of multispectral and thermal infrared data fusion in estimation of winter wheat yield[J]. Journal of Triticeae Crops, 2021, 41(12):1564-1572. (in Chinese with English abstract)
[27]	甄佳宁, 蒋侠朋, 赵德梅, 等. 利用Sentinel-2影像超分辨率重建的红树林冠层氮含量反演[J]. 遥感学报, 2022, 26(6):1206-1219.
	ZHEN J N, JIANG X P, ZHAO D M, et al. Retrieving canopy nitrogen content of mangrove forests from Sentinel-2 super-resolution reconstruction data[J]. National Remote Sensing Bulletin, 2022, 26(6):1206-1219. (in Chinese with English abstract)
[28]	王淑婷, 孔雨光, 张赞, 等. 基于星-机光谱融合的棉花叶片SPAD值反演[J]. 中国农业科学, 2022, 55(24):4823-4839.
	WANG S T, KONG Y G, ZHANG Z, et al. SPAD value inversion of cotton leaves based on satellite-UAV spectral fusion[J]. Scientia Agricultura Sinica, 2022, 55(24):4823-4839. (in Chinese with English abstract)
[29]	NYÉKI A, KEREPESI C, DARÓCZY B, et al. Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods[J]. Precision Agriculture, 2021, 22(5):1397-1415.
[30]	CHEN S, LIU W H, FENG P Y, et al. Improving spatial disaggregation of crop yield by incorporating machine learning with multisource data:a case study of Chinese maize yield[J]. Remote Sensing, 2022, 14(10):2340.
[31]	HAN J C, ZHANG Z, CAO J, et al. Prediction of winter wheat yield based on multi-source data and machine learning in China[J]. Remote Sensing, 2020, 12(2):236.
[32]	TRAMONTANA G, ICHⅡ K, CAMPS-VALLS G, et al. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data[J]. Remote Sensing of Environment, 2015, 168:360-373.
[33]	赵龙才, 李粉玲, 常庆瑞. 农作物遥感识别与单产估算研究综述[J]. 农业机械学报, 2023, 54(2):1-19.
	ZHAO L C, LI F L, CHANG Q R. Review on crop type identification and yield forecasting using remote sensing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(2):1-19. (in Chinese with English abstract)
[34]	郭晗, 陆洲, 徐飞飞, 等. 基于全局敏感性分析与机器学习的冬小麦叶面积指数估算[J]. 浙江农业学报, 2022, 34(9):2020-2031.
	GUO H, LU Z, XU F F, et al. Leaf area index estimation of winter wheat based on global sensitivity analysis and machine learning[J]. Acta Agriculturae Zhejiangensis, 2022, 34(9):2020-2031. (in Chinese with English abstract)
[35]	LI C X, CHIMIMBA E G, KAMBOMBE O, et al. Maize yield estimation in intercropped smallholder fields using satellite data in southern Malawi[J]. Remote Sensing, 2022, 14(10):2458.
[36]	卢佶, 张国威, 吴昊. 基于多时相光学和雷达遥感的太平湖生态保护区森林地上生物量反演[J]. 浙江农林大学学报, 2023, 40(5):1082-1092.
	LU J, ZHANG G W, WU H. Inversion study of above-ground biomass in Taiping Lake Ecological Reserve forests using multi-temporal optical and radar data[J]. Journal of Zhejiang A & F University, 2023, 40(5):1082-1092. (in Chinese with English abstract)
[37]	BALENZANO A, SATALINO G, LOVERGINE F P, et al. Sentinel-1 and sentinel-2 data to detect irrigation events:riaza irrigation district (Spain) case study[J]. Water, 2022, 14(19):3046.

植被指数Vegetation index	计算公式Calculation formula
差值植被指数Difference vegetation index (DVI)	ρ_NIR-ρ_R
增强植被指数2 Enhanced vegetation index without a blue band (EVI2)	2.5(ρ_NIR-ρ_R)/(ρ_NIR+2.4ρ_R+1)
绿色归一化植被指数Green normalized difference vegetation index (GNDVI)	(ρ_NIR-ρ_G)/(ρ_NIR+ρ_G)
比值植被指数Ratio vegetation index (RVI)	ρ_NIR/ρ_R
重归一化植被指数Renormalized difference vegetation index (RDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)^0.5
修正三角植被指数1 Modified triangular vegetation index (MTVI1)	1.2[1.2(ρ_NIR-ρ_G)-2.5(ρ_R-ρ_G)]
改进简单植被指数Modified simple ratio (MSR)	(ρ_NIR/ρ_R-1)/(ρ_NIR/ρ_R+1)^0.5
优化土壤调节植被指数Optimal soil adjusted vegetation index (OSAVI)	1.16(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.16)
结构加强色素植被指数Structure intensive pigment index (SIPI)	(ρ_NIR-ρ_B)/(ρ_NIR+ρ_B)
归一化差值水汽指数Normalized difference moisture index (NDMI)	(ρ_NIR-ρ_SWIR11)/(ρ_NIR+ρ_SWIR11)
绿色叶绿素指数Green chlorophyll vegetation index (CIg)	(ρ_NIR/ρ_R)-1
红边植被指数1 Red-edge chlorophyll index 1 (CIre1)	(ρ_NIR/ρ_RE1)-1
红边植被指数2 Red-edge chlorophyll index 2 (CIre2)	(ρ_NIR/ρ_RE2)-1
红边植被指数3 Red-edge chlorophyll index 3 (CIre3)	(ρ_NIR/ρ_RE3)-1
归一化植被指数Normalized difference vegetation index (NDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)
红边归一化植被指数1 Red-edge normalized difference vegetation index 1 (NDVIre1)	(ρ_NIR-ρ_RE1)/(ρ_NIR+ρ_RE1)
红边归一化植被指数2 Red-edge normalized difference vegetation index 2 (NDVIre2)	(ρ_NIR-ρ_RE2)/(ρ_NIR+ρ_RE2)
红边归一化植被指数3 Red-edge normalized difference vegetation index 3 (NDVIre3)	(ρ_NIR-ρ_RE3)/(ρ_NIR+ρ_RE3)

植被指数Vegetation index	计算公式Calculation formula
差值植被指数Difference vegetation index (DVI)	ρ_NIR-ρ_R
增强植被指数2 Enhanced vegetation index without a blue band (EVI2)	2.5(ρ_NIR-ρ_R)/(ρ_NIR+2.4ρ_R+1)
绿色归一化植被指数Green normalized difference vegetation index (GNDVI)	(ρ_NIR-ρ_G)/(ρ_NIR+ρ_G)
比值植被指数Ratio vegetation index (RVI)	ρ_NIR/ρ_R
重归一化植被指数Renormalized difference vegetation index (RDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)^0.5
修正三角植被指数1 Modified triangular vegetation index (MTVI1)	1.2[1.2(ρ_NIR-ρ_G)-2.5(ρ_R-ρ_G)]
改进简单植被指数Modified simple ratio (MSR)	(ρ_NIR/ρ_R-1)/(ρ_NIR/ρ_R+1)^0.5
优化土壤调节植被指数Optimal soil adjusted vegetation index (OSAVI)	1.16(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R+0.16)
结构加强色素植被指数Structure intensive pigment index (SIPI)	(ρ_NIR-ρ_B)/(ρ_NIR+ρ_B)
归一化差值水汽指数Normalized difference moisture index (NDMI)	(ρ_NIR-ρ_SWIR11)/(ρ_NIR+ρ_SWIR11)
绿色叶绿素指数Green chlorophyll vegetation index (CIg)	(ρ_NIR/ρ_R)-1
红边植被指数1 Red-edge chlorophyll index 1 (CIre1)	(ρ_NIR/ρ_RE1)-1
红边植被指数2 Red-edge chlorophyll index 2 (CIre2)	(ρ_NIR/ρ_RE2)-1
红边植被指数3 Red-edge chlorophyll index 3 (CIre3)	(ρ_NIR/ρ_RE3)-1
归一化植被指数Normalized difference vegetation index (NDVI)	(ρ_NIR-ρ_R)/(ρ_NIR+ρ_R)
红边归一化植被指数1 Red-edge normalized difference vegetation index 1 (NDVIre1)	(ρ_NIR-ρ_RE1)/(ρ_NIR+ρ_RE1)
红边归一化植被指数2 Red-edge normalized difference vegetation index 2 (NDVIre2)	(ρ_NIR-ρ_RE2)/(ρ_NIR+ρ_RE2)
红边归一化植被指数3 Red-edge normalized difference vegetation index 3 (NDVIre3)	(ρ_NIR-ρ_RE3)/(ρ_NIR+ρ_RE3)

组合方式Combination	特征变量Characteristic variable
A	Sentinel-1+Sentinel-2
B	Sentinel-2
C	Sentinel-1

组合方式Combination	特征变量Characteristic variable
A	Sentinel-1+Sentinel-2
B	Sentinel-2
C	Sentinel-1

回归模型 Regression model	组合方式 Combination	R²	RMSE/ (t·hm^-2)	nRMSE/%
XGBoost	A	0.654	0.499	10.02
	B	0.632	0.513	10.27
	C	0.539	0.592	11.86
RFR	A	0.473	0.534	10.71
	B	0.517	0.521	10.44
	C	0.311	0.673	13.49
SVR	A	0.340	0.866	17.36
	B	0.356	0.857	17.17
	C	0.213	1.303	26.11

Research on yield estimation method of winter wheat based on Sentinel-1/2 data and machine learning algorithms

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 37

Related Articles 15

Recommended Articles

Metrics

Comments

等级 Level/ (t·hm^-2)	冬小麦产量空间分布 Spatial distribution of winter wheat yield
等级 Level/ (t·hm^-2)	像元个数Number of pixels	所占比例Proportion/%
≤6	74 875	2.98
>6~7	447 252	17.78
>7~8	1 025 241	40.75
>8~9	891 461	35.43
>9	77 039	3.06

[1]	CHEN Yutiao, YAN Chuan, HONG Xiaofu, SONG Jiayu. Effects of submergence at tillering stage on growth characters, yield formation and potassium uptake of japonica inbred rice [J]. Acta Agriculturae Zhejiangensis, 2024, 36(9): 1990-1999.
[2]	MIN Jiangyan, TANG Zhuolei, YANG Xue, HUANG Xiaoyan, HUANG Kaifeng, HE Peiyun. Effect of different drought-rewatering modes on growth and yield of Tartary buckwheat [J]. Acta Agriculturae Zhejiangensis, 2024, 36(9): 2000-2009.
[3]	CHENG Jiayu, CHEN Miaojin, LI Tong, SUN Qinan, ZHANG Xiaobin, ZHAO Yiying, ZHU Yihang, GU Qing. Detection of peach trees in unmanned aerial vehicle (UAV) images based on improved Faster-RCNN network [J]. Acta Agriculturae Zhejiangensis, 2024, 36(8): 1909-1919.
[4]	SHEN Shengfa, XIANG Chao, MENG Yusha, LI Bing, WU Liehong. Breeding, yield and quality characteristics of Zheshu 86, a sweetpotato variety with high photosynthetic efficiency [J]. Acta Agriculturae Zhejiangensis, 2024, 36(7): 1469-1480.
[5]	PAN Zhijun, WU Xiaowen, WU Chenyang, CHENG Yu, CHEN Long, ZHANG Xiaohong, ZHANG Jinshan, ZHOU Bing, JIANG Bo, ZHANG Wenjing, CHE Zhao, SONG He. Analysis of yield and utilization of temperature and light resources of different types of ratoon rice varieties in central Anhui, China [J]. Acta Agriculturae Zhejiangensis, 2024, 36(7): 1492-1501.
[6]	LI Hui, TAN Xiaoqin, TANG Qian, YANG Yang, CHEN Wei. Effects of flower thinning on yield and quality components of Zi Yan tea plants [J]. Acta Agriculturae Zhejiangensis, 2024, 36(7): 1602-1615.
[7]	HU Tiejun. Effects of chemical fertilizer reduction combined with microbial fertilizer application on yield, quality, and soil properties of broccoli [J]. Acta Agriculturae Zhejiangensis, 2024, 36(7): 1657-1665.
[8]	XU Liting, QI Guangping, KANG Yanxia, YIN Minhua, MA Yanlin, JIA Qiong, WANG Jinghai, JIANG Yuanbo. Meta-analysis of effects of regulated deficit irrigation on yield and quality of alfalfa [J]. Acta Agriculturae Zhejiangensis, 2024, 36(6): 1256-1269.
[9]	ZHAO Liming, WANG Yaxin, JIANG Wenxin, DUAN Shaobiao, SHEN Xuefeng, ZHENG Dianfeng, FENG Naijie. Effects of plant growth regulators on yield, quality and photosynthetic characteristics of high-quality japonica rice [J]. Acta Agriculturae Zhejiangensis, 2024, 36(5): 1003-1014.
[10]	GAO Hu, MU Xiaoguo, LI Haijun, GAO Fucheng, ZHANG Ying, LI Jianshe, YE Lin. Effect of Fenlong tillage on soil characteristics and cabbage yield in dam land [J]. Acta Agriculturae Zhejiangensis, 2024, 36(5): 1113-1123.
[11]	CHEN Junlin, JIANG Na, LIU Xin, ZHUO Hong, TIAN Chang, HAN Yongliang, ZHANG Yuping, RONG Xiangmin. Effects of controlled-release nitrogen fertilizer reduction on crop yield, nitrogen absorption and runoff loss [J]. Acta Agriculturae Zhejiangensis, 2024, 36(4): 846-858.
[12]	LIAO Xuehuan, ZHANG Keyuan, AER Lise, ZHOU Lin, YANG Erluo, DENG Jun, ZHANG Rongping. Effects of branch fertilizer and functional fertilizer on growth and yield of hybrid rice [J]. Acta Agriculturae Zhejiangensis, 2024, 36(11): 2447-2455.
[13]	XU Chenyi, YANG Jiaxin, JIN Zhongyu, YU Fenghua. Research progress in polarization spectroscopy in the inversion of agronomic parameters of field crops [J]. Acta Agriculturae Zhejiangensis, 2024, 36(11): 2596-2604.
[14]	ZHANG Xujuan, ZHOU Liping, YU Kexin, CHAI Weiguo, PAN Nian, FU Xiaoxia, SHI Jianjun. Determination of annual variation of yield and main nutrients of green asparagus [J]. Acta Agriculturae Zhejiangensis, 2024, 36(10): 2238-2246.
[15]	LIU Zhicheng, YAN Liangwen, CHEN Yaoyao, OU Xueting. Yield and quality characteristics of passion fruit under different cultivation modes in Fujian, China [J]. Acta Agriculturae Zhejiangensis, 2024, 36(1): 134-147.