Classification of pre-winter wheat seedling conditions based on UAV images and local optimized features (LOFs)

doi:10.1016/j.jia.2025.07.031

Journal of Integrative Agriculture

0, Vol.

Issue (): 0- DOI: 10.1016/j.jia.2025.07.031

Advanced Online Publication | Current Issue | Archive | Adv Search

Classification of pre-winter wheat seedling conditions based on UAV images and local optimized features (LOFs)

Weijun Zhang^1,², Shaolong Zhu^1,², Dongwei Han^1,², Tianle Yang³, Yihan Jiang^1,², Jiacheng Wang^1,², Fei Wu⁴, Zhaosheng Yao⁵, Chengming Sun^1,²^#, Tao Liu^1,²^#

¹Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China

²Research Institute of Smart Agriculture, Yangzhou University, Yangzhou 225009, China

³Jiangsu Vocational College Of Agriculture And Forestry, Zhenjiang 212400, China

⁴Precision Agriculture Lab, School of Life Sciences, Technical University of Munich, Freising 85354, Bavaria, Germany

⁵Anqing Normal University, Anqing 246133, China

Highlights

Local optimized features (LOFs) were proposed to address the limitations of mean vegetation indices in seedling conditions classification.

Soil pixel removal and canopy cover integration laid the groundwork for precise pre-winter seedling classification and LOFs application.

An integrated feature set of PVIs, canopy cover, and LOFs achieved 0.99 accuracy using the QDA model.

Abstract
References

Download: PDF in ScienceDirect
Export: BibTeX | EndNote (RIS)

摘要小麦是全球范围内广泛消费的重要粮食作物。冬前苗情的良好与否直接决定了小麦产量的潜力，因此，及时而准确地掌握冬前苗情成为农业生产部门科学指导麦田管理的核心依据。目前，农业部门每年耗费大量人力与物力，根据叶龄、每亩总茎蘖数、单株一次茎蘖数及单株次生根数等农学指标，将麦苗划分为旺长苗、一类苗、二类苗和三类苗。然而，现阶段缺乏有效的高通量测量方法用于苗情分类。本研究明确了土壤像素去除和冠层覆盖度引入对于提升苗情分类模型精度的积极作用，并提出了一种称为局部优化特征（local optimized features， LOFs）的新方法来提升苗情分类精度，该方法通过定量描述比值植被指数（RVI）在局部的变化特征，弥补了均值植被指数在感知小麦单株性状方面的不足。本研究通过2022至2024年间的播期处理，构建了不同苗情的小麦群体，利用无人机获取了高分辨率的冬前小麦多光谱图像，并提取了植被指数（VIs）、纯植被指数（PVIs）与冠层覆盖度（cc）等传统遥感参数。通过对多种分类模型的性能评估，确定了PVIs与cc的组合为最优特征集，并通过Shapley加性解释模型（SHAP）筛选出关键植被指数RVI。最终，本研究引入LOFs构建了融合PVIs、cc和LOFs的二次判别分析（QDA）模型，实现了苗情的高精度分类，将分类准确率从0.86提升至0.99。本研究为实现越冬前小麦苗情的高通量、高精度分类提供了有效工具，并为其他农艺参数的遥感估测提供了技术参考。

Abstract

Wheat is a vital global staple crop, and the condition of its seedlings before overwintering significantly influences its yield potential. Accurate and timely assessment of pre-winter seedling conditions is essential for effective wheat field management. Currently, agricultural departments rely on traditional methods to classify seedlings based on indicators like leaf age, tiller count, and root number, but these methods are labor-intensive and lack high-throughput capabilities. This study proposes a novel approach to improve seedling condition classification by integrating soil pixel removal and canopy cover with vegetation indices. Additionally, a local optimized features (LOFs) method is introduced to enhance classification by quantifying local spectral differences in the ratio vegetation index (RVI), overcoming the limitations of traditional mean vegetation indices. A series of sowing date treatments from 2022 to 2024 established wheat populations with varied seedling conditions. High-resolution multispectral UAV imagery was used to derive remote sensing parameters, such as vegetation indices (VIs), pure vegetation indices (PVIs), and canopy cover (cc). Through evaluation of various classification models, we identified PVIs combined with cc as the optimal feature set. Among these, RVI was found to be the most significant index, as determined by SHapley Additive exPlanations (SHAP). Building upon the optimal feature set, a Quadratic Discriminant Analysis model integrating PVIs, cc, and LOFs was ultimately developed to achieve accurate classification of seedling conditions, improving the accuracy from 0.86 (with PVIs and cc) to 0.99. This research provides an efficient high-throughput method for pre-winter seedling classification and offers insights into estimating other agronomic parameters.

Keywords: wheat seedling conditions multispectral local optimized features

Online: 29 July 2025

Fund:

This work was supported by the National Natural Science Foundation of China (32172110), the National Key Research and Development Program of China (2023YFD2300201), the Biological Breeding Zhongshan Laboratory Program of Jiangsu Province, China (ZSBBL-KY2023-05), the Key Research and Development Program (Modern Agriculture) of Jiangsu Province, China (BE2022335, BE2022338, BE2022342-2, and BE2020319), the Central Public-interest Scientific Institution Basal Research Fund, China (JBYW-AII2023-08), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (KYCX22_3513).

About author: Weijun Zhang, E-mail: 839784491@qq.com; #Correspondence Chengming Sun, E-mail:cmsun@yzu.edu.cn; Tao Liu, E-mail: tliu@yzu.edu.cn

	Service
	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

Weijun Zhang, Shaolong Zhu, Dongwei Han, Tianle Yang, Yihan Jiang, Jiacheng Wang, Fei Wu, Zhaosheng Yao, Chengming Sun, Tao Liu. 2025. Classification of pre-winter wheat seedling conditions based on UAV images and local optimized features (LOFs). Journal of Integrative Agriculture, Doi:10.1016/j.jia.2025.07.031

Ashraf T, Khan Y N. 2020. Weed density classification in rice crop using computer vision. Computers and Electronics in Agriculture, 175, 105590.

Assmann J J, Kerby J T, Cunliffe A M, Myers-Smith I H. 2019. Vegetation monitoring using multispectral sensors — best practices and lessons learned from high latitudes. Journal of Unmanned Vehicle Systems, 7, 54–75.

Awais M, Li W, Cheema M J M, Hussain S, Shaheen A, Aslam B, Liu C, Ali A. 2022. Assessment of optimal flying height and timing using high-resolution unmanned aerial vehicle images in precision agriculture. International Journal of Environmental Science and Technology, 19, 2703–2720.

Bastos L M, Carciochi W, Lollato R P, Jaenisch B R, Rezende C R, Schwalbert R, Vara Prasad P V, Zhang G R, Fritz A K, Foster C, Wright Y, Young S, Bradley P, Ciampitti I A. 2020. Winter wheat yield response to plant density as a function of yield environment and tillering potential: A review and field studies. Frontiers in Plant Science, 11, 54.

Cao W X, Li C D. 1997. A sequential naming scheme for developmental organs in wheat. Scientia Agricultura Sinica, 30, 66–70. (in Chinese)

Cui J R, Zheng H, Zeng Z W, Yang Y L, Ma R J, Tian Y Y, Tan J W, Feng X, Qi L. 2023. Real-time missing seedling counting in paddy fields based on lightweight network and tracking-by-detection algorithm. Computers and Electronics in Agriculture, 212, 108045.

DB32/T 2325—2013. 2013. The Classification of Wheat Seedlings and Its Regulating Technique Rules. Jiangsu Provincial Bureau of Quality and Technical Supervision, Local Standard of Jiangsu Province, China. (in Chinese)

Ding J F, Li F J, Xu D Y, Wu P, Zhu M, Li C Y, Zhu X K, Chen Y L, Guo W S. 2021. Tillage and nitrogen managements increased wheat yield through promoting vigor growth and production of tillers. Agronomy Journal, 113, 1640–1652.

Ding Y G, Zhang X B, Ma Q, Li F J, Tao R R, Zhu M, Li C Y, Zhu X K, Guo W S, Ding J F. 2023. Tiller fertility is critical for improving grain yield, photosynthesis, and nitrogen efficiency in wheat. Journal of Integrative Agriculture, 22, 2054–2066.

Friedman J H. 2001. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29, 1189–1232.

GB/T 37804—2019. 2019. Specifications for winter wheat growth monitoring. State Administration for Market Regulation of China, National Standard of the People’s Republic of Chin. (in Chinese)

Han S W, Wang C X, Zheng Y S, Lu Z G, Dang Y P, Si J S, Li H W, Zhao X, Zhang H L. 2024. The yield and nitrogen use efficiency of winter wheat in the north China plain could be improved through enhanced tiller formation and biomass transport. Field Crops Research, 318, 109570.

Hou P, Gao Q, Ren Y K, Yu J H, Gao L J, Liu X X, Jiang D, Cao W X, Dai T B, Tian Z W. 2024. Straw returning and night-warming improve grain yield and nitrogen use efficiency of winter wheat under rice-wheat rotation. Journal of Integrative Agriculture.

Hu Y C, Knapp S, Schmidhalter U. 2020. Advancing high-throughput phenotyping of wheat in early selection cycles. Remote Sensing, 12, 574.

Huang J X, Tian L Y, Liang S L, Ma H Y, Becker-Reshef I, Huang Y B, Su W, Zhang X D, Zhu D H, Wu W B. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106–121.

Huete A R, Jackson R D, Post D F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment, 17, 37–53.

Jadhav S D, Channe H P. 2016. Comparative study of K-NN, naive bayes and decision tree classification techniques. International Journal of Science and Research, 5, 1842–1845.

Jay S, Baret F, Dutartre D, Malatesta G, Héno S, Comar A, Weiss M, Maupas F. 2019. Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sensing of Environment, 231, 110898.

Jiang X Y, Mao D D, Zhu M, Wang X C, Li C Y, Zhu X K, Guo W S, Ding J F. 2022. Evaluating the waterlogging tolerance of wheat cultivars during the early growth stage using the comprehensive evaluation value and digital image analysis. Agriculture, 12, 384.

Jin X L, Zarco-Tejada P J, Schmidhalter U, Reynolds M P, Hawkesford M J, Varshney R K, Yang T, Nie C W, Li Z H, Ming B, Xiao Y G, Xie Y D, Li S K. 2021. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine, 9, 200–231.

Jones J R, Fleming C S, Pavuluri K, Alley M M, Reiter M S, Thomason W E. 2015. Influence of soil, crop residue, and sensor orientations on NDVI readings. Precision Agriculture, 16, 690–704.

Kipp S, Mistele B, Baresel P, Schmidhalter U. 2014. High-throughput phenotyping early plant vigour of winter wheat. European Journal of Agronomy, 52, 271–278.

Kishore B, Yasar A, Taspinar Y S, Kursun R, Cinar I, Shankar V G, Koklu M, Ofori I. 2022. Computer-aided multiclass classification of corn from corn images integrating deep feature extraction. Computational Intelligence and Neuroscience, 2022, 2062944.

Large E C. 1954. Growth stages in cereals illustration of the feekes scale. Plant Pathology, 3, 128–129.

Li C C, Wang J, Wang L, Hu L Y, Gong P. 2014. Comparison of classification algorithms and training sample sizes in urban land classification with landsat thematic mapper imagery. Remote Sensing, 6, 964–983.

Li F J, Zhang X B, Xu D Y, Ma Q, Le T, Zhu M, Li C Y, Zhu X K, Guo W S, Ding J F. 2022. No-tillage promotes wheat seedling growth and grain yield compared with plow–rotary tillage in a rice–wheat rotation in the high rainfall region in China. Agronomy, 12, 865.

Li H L, Zhao C J, Yang G J, Feng H K. 2015. Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes. Remote Sensing of Environment, 169, 358–374.

Li H M, Zhang J C, Xu K, Jiang X P, Zhu Y, Cao W X, Ni J. 2021a. Spectral monitoring of wheat leaf nitrogen content based on canopy structure information compensation. Computers and Electronics in Agriculture, 190, 106434.

Li Y X, Liu H J, Ma J C, Zhang L X. 2021b. Estimation of leaf area index for winter wheat at early stages based on convolutional neural networks. Computers and Electronics in Agriculture, 190, 106480.

Liu P, Yin B Z, Liu X J, Gu L M, Guo J K, Yang M M, Zhen W C. 2023a. Optimizing plant spatial competition can change phytohormone content and promote tillering, thereby improving wheat yield. Frontiers in Plant Science, 14, 1147711.

Liu S B, Jin X L, Bai Y, Wu W B, Cui N B, Cheng M H, Liu Y D, Meng L, Jia X, Nie C W, Yin D M. 2023b. UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background. International Journal of Applied Earth Observation and Geoinformation, 121, 103383.

Liu T, Li R, Zhong X C, Jiang M, Jin X L, Zhou P, Liu S P, Sun C M, Guo W S. 2018a. Estimates of rice lodging using indices derived from UAV visible and thermal infrared images. Agricultural and Forest Meteorology, 252, 144–154.

Liu T, Yang T L, Li C Y, Li R, Wu W, Zhong X C, Sun C M, Guo W S. 2018b. A method to calculate the number of wheat seedlings in the 1st to the 3rd leaf growth stages. Plant Methods, 14, 101.

Liu T, Yang T L, Zhu S L, Mou N N, Zhang W J, Wu W, Zhao Y Y, Yao Z S, Sun J J, Chen C, Sun C M, Zhang Z J. 2024. Estimation of wheat biomass based on phenological identification and spectral response. Computers and Electronics in Agriculture, 222, 109076.

Liu T, Zhao Y, Wu F, Wang J, Chen C, Zhou Y, Ju C, Huo Z, Zhong X, Liu S, Sun C. 2023c. The estimation of wheat tiller number based on UAV images and gradual change features (GCFs). Precision Agriculture, 24, 353–374.

Liu Y, An L L, Wang N, Tang W J, Liu M J, Liu G H, Sun H, Li M Z, Ma Y T. 2023d. Leaf area index estimation under wheat powdery mildew stress by integrating UAV‑based spectral, textural and structural features. Computers and Electronics in Agriculture, 213, 108169.

Lu D J, Yue S C, Lu F F, Cui Z L, Liu Z H, Zou C Q, Chen X P. 2016. Integrated crop-N system management to establish high wheat yield population. Field Crops Research, 191, 66–74.

Magurran A E. 2021. Measuring biological diversity. Current Biology, 31, R1174–R1177.

Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi F B. 2020. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599.

Marino S, Alvino A. 2021. Vegetation indices data clustering for dynamic monitoring and classification of wheat yield crop traits. Remote Sensing, 13, 541.

Miller T D. 1992. Growth stages of wheat. Better crops with plant food. Potash & Phosphate Institute, 76, 12.

Mu X H, Yang Y, Xu H, Guo Y H, Lai Y K, McVicar T R, Xie D H, Yan G J. 2024. Improvement of NDVI mixture model for fractional vegetation cover estimation with consideration of shaded vegetation and soil components. Remote Sensing of Environment, 314, 114409.

Ozdogan M, Woodcock C E. 2006. Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment, 103, 203–217.

Peake A S, Bell K L, Fischer R A, Gardner M, Das B T, Poole N, Mumford M. 2020. Cultivar×management interaction to reduce lodging and improve grain yield of irrigated spring wheat: Optimising plant growth regulator use, N application timing, row spacing and sowing date. Frontiers in Plant Science, 11, 401.

Pielou E C. 1966. The measurement of diversity in different types of biological collections. Journal of Theoretical Biology, 13, 131–144.

Pu R L, Landry S. 2012. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment, 124, 516–533.

Qiao L, Tang W J, Gao D H, Zhao R M, An L L, Li M Z, Sun H, Song D. 2022. UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages. Computers and Electronics in Agriculture, 196, 106775.

Ren A X, Sun M, Wang P R, Xue L Z, Lei M, Xue J F, Gao Z Q, Yang Z P. 2019. Optimization of sowing date and seeding rate for high winter wheat yield based on pre-winter plant development and soil water usage in the Loess Plateau, China. Journal of Integrative Agriculture, 18, 33–42.

Saberioon M M, Amin M S M, Anuar A R, Gholizadeh A, Wayayok A, Khairunniza-Bejo S. 2014. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, 32, 35–45.

Shah F, Coulter J A, Ye C, Wu W. 2020. Yield penalty due to delayed sowing of winter wheat and the mitigatory role of increased seeding rate. European Journal of Agronomy, 119, 126120.

Shirazi S Z, Mei X R, Liu B C, Liu Y. 2022. Estimating potential yield and change in water budget for wheat and maize across huang-huai-hai plain in the future. Agricultural Water Management, 260, 107282.

Sticksel E, Schächtl J, Huber G, Liebler J, Maidl F X. 2004. Diurnal variation in hyperspectral vegetation indices related to winter wheat biomass formation. Precision Agriculture, 5, 509–520.

Sun H W, Wang Y H, Wang L. 2024. Impact of climate change on wheat production in China. European Journal of Agronomy, 153, 127066.

Tang L N, Shao G F. 2015. Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26, 791–797.

Tian L, Wang Z Y, Xue B W, Li D, Zheng H B, Yao X, Zhu Y, Cao W X, Cheng T. 2023. A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space. Remote Sensing of Environment, 285, 113384.

Wang F H, He Z H, Sayre K, Li S D, Si J S, Feng B, Kong L G. 2009. Wheat cropping systems and technologies in China. Field Crops Research, 111, 181–188.

Wang J J, Zhou Q, Shang J L, Liu C, Zhuang T X, Ding J J, Xian Y Y, Zhao L T, Wang W L, Zhou G S, Tan C W, Huo Z Y. 2021. UAV- and machine learning-based retrieval of wheat SPAD values at the overwintering stage for variety screening. Remote Sensing, 13, 5166.

Wang J, Plataniotis K N, Lu J W, Venetsanopoulos A N. 2008. Kernel quadratic discriminant analysis for small sample size problem. Pattern Recognition, 41, 1528–1538.

Xie Y. 2022. Combining CERES-Wheat model, Sentinel-2 data, and deep learning method for winter wheat yield estimation. International Journal of Remote Sensing, 43, 630–648.

Xu C, Zeng Y, Zheng Z J, Zhao D, Liu W J, Ma Z H, Wu B F. 2022. Assessing the impact of soil on species diversity estimation based on UAV imaging spectroscopy in a natural alpine steppe. Remote Sensing, 14, 671.

Yang Y D, Li Q, Mu Y, Li H T, Wang H T, Ninomiya S, Jiang D. 2024. UAV-assisted dynamic monitoring of wheat uniformity toward yield and biomass estimation. Plant Phenomics, 6, 0191.

Zhang G P, Chen J X, David Augustine B. 2001. The effects of timing of N application and plant growth regulators on morphogenesis and yield formation in wheat. Plant Growth Regulation, 35, 239–245.

Zhang G, Zhao D, Fan H, Liu S, Liao Y, Han J. 2023. Combining controlled-release urea and normal urea with appropriate nitrogen application rate to reduce wheat stem lodging risk and increase grain yield and yield stability. Journal of Integrative Agriculture, 22, 3006–3021.

Zhang L Y, Yuan D B, Fan Y Q, Yang R X, Zhao M C, Jiang J B, Zhang W X, Huang Z Y, Ye G D, Li W N. 2024. Hyperspectral estimation of chlorophyll content in wheat under CO₂ stress based on fractional order differentiation and continuous wavelet transforms. Remote Sensing, 16, 3341.

Zhu S L, Zhang W J, Yang T L, Wu F, Jiang Y H, Yang G S, Zain M, Zhao Y Y, Yao Z S, Liu T, Sun C M. 2024. Combining 2D image and point cloud deep learning to predict wheat above ground biomass. Precision Agriculture, 25, 3139–3166.

[1]	Qi Zhao, Mengjie Cui, Tengda Guo, Lei Shi, Feiyan Qi, Ziqi Sun, Pei Du, Hua Liu, Yu Zhang, Zheng Zheng, Bingyan Huang, Wenzhao Dong, Suoyi Han, Xinyou Zhang. Genome-wide characterization and expression analysis of the cultivated peanut AhPR10 gene family mediating resistance to Aspergillus flavus[J]. >Journal of Integrative Agriculture, 2026, 25(1): 56-67.
[2]	Kegu Ji’e, Falong Yang, Ai Ran, Yang Su, Taichun Gao, Lanmuyi Gou, Shenglin Li, Zihan Xia, Keha-mo Abi. *A novel Bocaparvovirus* in goats: Genetic features, phylogenetic analysis, and epidemiological implications**[J]. >Journal of Integrative Agriculture, 2025, 24(8): 3291-3296.
[3]	Yuan Gao, Fuxia Bai, Qi Zhang, Xiaoya An, Zhaofei Wang, Chuzhao Lei, Ruihua Dang. Dynamic transcriptome profiles and novel markers in bovine spermatogenesis revealed by single-cell sequencing[J]. >Journal of Integrative Agriculture, 2024, 23(7): 2362-2378.
[4]	Shuang Cheng, Zhipeng Xing, Chao Tian, Mengzhu Liu, Yuan Feng, Hongcheng Zhang. Optimized tillage methods increase mechanically transplanted rice yield and reduce the greenhouse gas emissions [J]. >Journal of Integrative Agriculture, 2024, 23(4): 1150-1163.
[5]	Liyao Su, Min Wu, Tian Zhang, Yan Zhong, Zongming (Max) Cheng. *Identification of key genes regulating the synthesis of quercetin derivatives in Rosa* roxburghii through integrated transcriptomics and metabolomics** [J]. >Journal of Integrative Agriculture, 2024, 23(3): 876-887.
[6]	Mosses Lufuke, Xu Tian. Women’s empowerment and food consumption: Evidence from female-headed households in Tanzania [J]. >Journal of Integrative Agriculture, 2024, 23(2): 457-467.
[7]	Binbin Li, Xianmin Chen, Tao Deng, Xue Zhao, Fang Li, Bingchao Zhang, Xin Wang, Si Shen, Shunli Zhou. Timing effect of high temperature exposure on the plasticity of internode and plant architecture in maize [J]. >Journal of Integrative Agriculture, 2024, 23(2): 551-565.
[8]	ZHUANG Hui, LAN Jin-song, YANG Qiu-ni, ZHAO Xiao-yu, LI Yu-huan, ZHI Jing-ya, SHEN Ya-lin, HE Guang-hua, LI Yun-feng. **SUPER WOMAN 2 (SPW2) maintains organ identity in spikelets by inhibiting the expression of floral homeotic genes OsMADS3, OsMADS58, OsMADS13, and DROOPING LEAF**[J]. >Journal of Integrative Agriculture, 2024, 23(1): 59-76.
[9]	Qiuyan Yan, Linjia Wu, Fei Dong, Shuangdui Yan, Feng Li, Yaqin Jia, Jiancheng Zhang, Ruifu Zhang, Xiao Huang. Subsoil tillage enhances wheat productivity, soil organic carbon and available nutrient status in dryland fields [J]. >Journal of Integrative Agriculture, 2024, 23(1): 251-266.
[10]	SONG Jin-xing, WANG Meng-xiang, ZHANG Yi-xuan, WAN Bo, DU Yong-kun, ZHUANG Guo-qing, LI Zi-bin, QIAO Song-lin, GENG Rui, WU Ya-nan, ZHANG Gai-ping. Identification and epitope mapping of anti-p72 single-chain antibody against African swine fever virus based on phage display antibody library[J]. >Journal of Integrative Agriculture, 2023, 22(9): 2834-2847.
[11]	WANG Xue-feng, SHAO Dong-nan, LIANG Qian, FENG Xiao-kang, ZHU Qian-hao, YANG Yong-lin, LIU Feng, ZHANG Xin-yu, LI Yan-jun, SUN Jie, XUE Fei. *A 2-bp frameshift deletion at GhDR, which encodes a B-BOX protein that co-segregates with the dwarf-red phenotype in Gossypium* hirsutum L.**[J]. >Journal of Integrative Agriculture, 2023, 22(7): 2000-2014.
[12]	GAO Yue, LUO Jian, SUN Yue, ZHANG Hua-wei, ZHANG Da-xia, LIU Feng, MU Wei, LI Bei-xing. Photosensitivity and a precise combination of size-dependent lambda-cyhalothrin microcapsules synergistically generate better insecticidal efficacy [J]. >Journal of Integrative Agriculture, 2023, 22(5): 1477-1488.
[13]	JIN Ji-su, LIU Yi-ran, ZHOU Zhong-shi, WAN Fang-hao, GUO Jian-ying. *Halloween genes AhCYP307A2* and AhCYP314A1 modulate last instar larva–pupa–adult transition, ovarian development and oogenesis in Agasicles hygrophila (Coleoptera: Chrysomelidae)**[J]. >Journal of Integrative Agriculture, 2023, 22(3): 812-824.
[14]	WANG Fei-bing, WAN Chen-zhong, NIU Hao-fei, QI Ming-yang, LI Gang, ZHANG Fan, HU Lai-bao, YE Yu-xiu, WANG Zun-xin, PEI Bao-lei, CHEN Xin-hong, YUAN Cai-yuan. OsMas1, a novel maspardin protein gene, confers tolerance to salt and drought stresses by regulating ABA signaling in rice [J]. >Journal of Integrative Agriculture, 2023, 22(2): 341-359.
[15]	PAN Fan, GAO Li-jie, ZHU Kai-hui, DU Gui-lin, ZHU Meng-meng, ZHAO Li, GAO Yu-lin, TU Xiong-bing, ZHANG Ze-hua. Regional selection of insecticides and fungal biopesticides to control aphids and thrips and improve the forage quality of alfalfa crops[J]. >Journal of Integrative Agriculture, 2023, 22(1): 185-194.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed

Cite this article:

About JIA

Editorial board

For authors