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For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Detection Method of Sesame Capsules Based on Improved YOLOX Model WANG Chuan, ZHAO Hengbin, LI Guoqiang, ZHANG Jiantao, GAO Tongmei, ZHAO Qiaoli, ZHENG Guoqing Journal of Henan Agricultural Sciences    2022, 51 (11): 155-162.   DOI: 10.15933/j.cnki.1004-3268.2022.11.018 Abstract （830）      PDF （7366KB）（82）       Knowledge map    Save In order to achieve accurate detection of sesame capsules under dense conditions，this study proposes a sesame capsule detection and localization method based on the YOLOX model（CE‑YOLOX model）.In this model，CSPDarknet‑53 is used as the backbone feature extraction network，and a 104×104 large‑scale feature layer is added to the path aggregation network PANet to strengthen the acquisition of the target fine‑grained feature information.By introducing the convolutional block attention module，the important contour features and spatial location information of the object are obtained.The classical NMS is replaced by the Soft‑NMS algorithm，which is more conducive to overlapping target detection，to decrease the missed detection.The results showed that the F1 average of CE‑YOLOX tested on all datasets at IoU threshold of 0.5 was 0.99，0.05 higher than that of YOLOX.The recall rate and average accuracy of CE‑YOLOX were 98.65% and 99.71%，6.27 and 3.28 percentage points higher than that of YOLOX.The counting accuracy of CE‑YOLOX was 96.84%，5.28 percentage points higher than YOLOX.Consequently，the improved model can recognize sesame capsules under dense conditions. Reference | Related Articles | Metrics Select Irrigation Strategies Optimization for Winter Wheat in Henan Province Based on AquaCrop Model RONG Yasi, LI Guoqiang, ZHANG Jie, ZHANG Jiantao, WANG Meng, ZHENG Guoqing, FENG Wei Journal of Henan Agricultural Sciences    2023, 52 (2): 151-161.   DOI: 10.15933/j.cnki.1004-3268.2023.02.017 Abstract （1009）      PDF （1465KB）（98）       Knowledge map    Save Crop simulation models are practical tools for assessing and developing irrigation strategies.In this study，the AquaCrop model was calibrated and validated for wheat in Henan province.The validated model was then applied to investigate the effect of variable irrigation strategies on wheat yield.The model calibration was performed on field experiments at Zhoukou of Henan Province during the 2016—2018 growing season.Afterward，the validation was done on field experiments during the 2019—2020 growing season.After accurate calibration and validation of the AquaCrop model，the effects of 16 irrigation scenarios on water consumption，transpiration，grain yield，water use efficiency and irrigation water use efficiency of winter wheat under different rainfall year types were analyzed.The results showed that various parameters affecting canopy cover and grain yield had been calibrated based on the comparison between measurements and the results of simulations.The results of canopy cover such as R2，d，and RMSE were 0.84 to 0.94，0.93 to 0.98，and 4.7% to 9.4%，respectively.The reliability indices of biomass were 0.94―0.95，0.93―0.98，and 2.1―2.2 t/ha，respectively.Moreover，the RE of yield and water use efficiency was lower than 10%，respectively.The various simulations（irrigation scenarios）showed that the optimal irrigation strategies achieved the maximum grain yield and water use efficiency in different rainfall patterns.In dry years，225 mm of irrigation water was applied at the jointing，flowering，and grain‑filling stages.In normal years，150 mm of irrigation water was applied at the jointing and flowering stages. In wet years，75 mm of irrigation water was applied at the jointing stage. Reference | Related Articles | Metrics Select Study on Canopy Chlorophyll Estimation Model of Buckwheat with Different Selenium Levels Based on UAV Multispectrum MA Wei, WU Zhiming, YU Kesong Journal of Henan Agricultural Sciences    2023, 52 (3): 161-172.   DOI: 10.15933/j.cnki.1004-3268.2023.03.018 Abstract （883）      PDF （3820KB）（114）       Knowledge map    Save In order to verify the possibility of estimating the chlorophyll content of buckwheat canopy by using low‑altitude unmanned aerial vehicle（UAV）with multi‑spectral camera，and to explore the effect of selenium（Se）on the chlorophyll and spectral characteristics of buckwheat canopy，Jinsage No.6 sweet buckwheat and Jinsage No.9 bitter buckwheat were selected as the research objects，and a experimental study was conducted in Taigu District，Jinzhong City，Shanxi Province.Buckwheat multispectral images were collected under different selenium levels at different stages by using UAV with multi‑spectral camera，and the relative chlorophyll content（SPAD value）was synchronously measured in the field.Firstly，the SPAD values of buckwheat canopy under different selenium application levels were analyzed.Secondly，the reflectance of buckwheat canopy under five bands was obtained by extracting the spectral information of multi‑spectral images.On this basis，the spectral characteristics of buckwheat at full bloom and filling stage were analysed，11 vegetation indices were constructed using the reflectance in five bands，and the absolute magnitude of the correlation coefficients between the 16 spectral variables and the measured SPAD values were obtained by Pearson correlation analysis.Partial least square regression（PLSR），principal component regression（PCR），support vector machine regression（SVR）and back propagation neural network（BPNN）were used to construct a buckwheat canopy SPAD monitoring model，and the optimal estimation model was determined by accuracy test.The results showed that moderate application of selenium fertilizer could increase SPAD value of buckwheat，while excessive application could inhibit SPAD value.The blue，red，red‑edge and NIR bands showed strong correlation among the five bands，and the NIR band showed high and stable correlation. In terms of vegetation index，the correlation coefficients（|r|）of standardized precipitation index（SPI），green chlorophyll index（GCI），green normalized difference vegetation index（GNDVI），normalized green light index（NGI），transformed optimized soil‑regulated vegetation index（TOSAVI），transformed chlorophyll absorption ratio index（TCARI），and triangular vegetation index（TVI）ranged from 0. 50 to 0. 91，which indicated a good correlation.At the full‑bloom stage，BPNN had the best prediction effect，the correlation coefficient of prediction set（R2P）was 0.97，and the root mean square error（RMSE）of prediction set was 0.95.In the filling stage，the prediction effect of SVR was better than other models，R2Pwas 0.96，and RMSE was 0.45.At the full‑bloom stage and filling stage，PLSR showed the best performance，R2P was 0.98，RMSE was 0.28.By comparing all the models，SVR showed higher stability and accuracy（R2P and RMSE ranges were 0.94—0.96 and 0.45—0.82，respectively，and RPD values were greater than 3.00）.These results show that UAV with a multispectral camera can achieve rapid monitoring of buckwheat canopy chlorophyll content in the field，providing a reference for optimization of the model algorithm for low altitude prediction of chlorophyll content by UAV. Reference | Related Articles | Metrics Select Influence of Temperature and Moisture Content on Thermophysical Properties of Tobacco Leaves and Establishment of Prediction Models CHEN Jiading, HE Rong, XIAO Qingli, YUAN Ming, TAN Qizhong, PENG Kui, WEI Shuo, LI Shengchun Journal of Henan Agricultural Sciences    2023, 52 (6): 172-180.   DOI: 10.15933/j.cnki.1004-3268.2023.06.018 Abstract （1078）      PDF （3074KB）（415）       Knowledge map    Save In order to comprehensively understand the thermophysical properties of different components of tobacco leaves and provide reference for the design of thermal processing technology such as baking，moisture regain and redrying，the thermal diffusion coefficient，thermal conductivity and mass specific heat capacity of post⁃baking tobacco leaves and main veins were measured by the thermal probe method under the temperature from 10 to 70℃ and moisture content from 5% to 30%，respectively，and the variation pattern was analyzed.The correlation between temperature，moisture content and the values of thermophysical properties of tobacco leaves was fitted in the form of exponential function with the help of MATLAB software to construct an empirical mathematical model of the thermophysical properties of tobacco leaves，which was verified by experiments.The results showed that，（1）The density of tobacco leaves and main veins increased with the increase of moisture content.When the moisture content was 5%—30%，the density of leaves was 562.79—684.84 kg/m3，and the density of main veins was 908.83 to 1 045.51 kg/m3；（2）The increase of temperature and moisture content would increase the thermal diffusion coefficient of tobacco leaves and main veins，and the contribution rate of moisture content was greater.The thermal diffusion coefficient of leaves was 0.092 33—0.219 00 mm2/s，and the thermal diffusion coefficient of main veins was 0.088 67—0.149 00 mm2/s；（3）The thermal conductivity of tobacco leaves and main veins increased with the increase of temperature and moisture content.The thermal conductivity of leaves was 0.088 13—0.435 37 W（/m·K），and the thermal conductivity of main veins was 0.160 70—0.388 83 W（/m·K）；（4）The mass specific heat capacity of tobacco leaves and main veins was mainly positively related to the moisture content. The mass specific heat capacity of tobacco leaves was 1 520.623 44—3 123.569 52 J（/kg·K），and the mass specific heat capacity of main veins was 1 618.828 08—2 563.703 20 J（/kg·K）.（5）For verifying the fitted empirical model，R2 was between 0.93 and 0.99.In summary，the thermophysical properties of leaves and main veins differed greatly，and the fitted exponential function could be used as an empirical mathematical model for calculating the corresponding thermophysical property parameters of tobacco leaves and main veins. Reference | Related Articles | Metrics Select Rice Phenotypic Parameters Extraction and Biomass Estimation Based on Three⁃Dimensional Model CHENG Zhiqiang, FANG Shenghui Journal of Henan Agricultural Sciences    2023, 52 (7): 144-153.   DOI: 10.15933/j.cnki.1004-3268.2023.07.015 Abstract （1122）      PDF （6848KB）（438）       Knowledge map    Save The phenotype detection methods based on two⁃dimensional images lack spatial dimension information and have difficulty in extracting comprehensive rice phenotype parameters.Therefore，it is of great significance to establish a three⁃dimensional model of rice to extract comprehensive rice phenotype parameters.The volume is an important parameter indicating the growth status of rice. How to obtain rice volume parameters without damaging the rice plant is still a problem to be solved in current research.Based on the above considerations，a method of rice phenotypic parameters extraction and biomass estimation based on three⁃dimensional model is proposed in this paper，which can extract rice volume parameters and estimate rice biomass without damaging the rice plant.This study focused on potted rice，first reconstructed its three⁃dimensional model using the Alpha⁃shape algorithm，and then extracted and evaluated rice height，stem thickness，vegetation coverage，volume parameters，and estimated rice biomass based on the volume parameter.The experimental results showed that space carving could reconstruct a high⁃precision three⁃dimensional model of rice and accurately measure rice phenotype parameters and biomass，the RMSE and MAPE of rice height，stem thickness，and single plant vegetation coverage were 63.27 mm，4.01 mm，5.04% and 7.15%，14.91%，12.59%，and the RMSE and MAPE of rice biomass were 62.44 g and 19.25%，which were better than the results obtained by existing three⁃dimensional reconstruction software. Reference | Related Articles | Metrics Select Study on Spatio‑Temporal Variation of Climatic Suitability of Sesame Planting in Henan Province HU Feng, ZHANG Jiantao, ZHANG Jie, GAO Tongmei, ZHAO Qiaoli, ZHENG Guoqing, LI Guoqiang, LIU Lijie Journal of Henan Agricultural Sciences    2023, 52 (8): 56-68.   DOI: 10.15933/j.cnki.1004-3268.2023.08.007 Abstract （941）      PDF （3493KB）（238）       Knowledge map    Save This study aimed to clarify the spatial and temporal distribution characteristics of the climatic suitability of sesame in Henan Province and to guide the layout of sesame production by establishing climate suitability models for sesame growth using fuzzy mathematics method based on the light，temperature，and water requirements of sesame.The temperature，light，precipitation，and comprehensive climate suitability of sesame were calculated using the daily meteorological data of 15 meteorological stations in Henan from 1961 to 2019.The results showed that temperature suitability（0.938—0.956）was higher than light suitability（0.568—0.657）and precipitation suitability（0.492—0.595）of sesame in 15 stations in Henan Province，and the comprehensive climate suitability was 0.641—0.712.From 1961 to 2019，the light and comprehensive climate suitability of sesame in Henan Province showed asignificant downward trend，decreasing by 0.032 and 0.012 every 10 years.The change trends of temperature and precipitation suitability were not significant.The light，precipitation，and comprehensive climate suitability decreased gradually from north to south，while the temperature suitability increased gradually from northwest to southeast. The findings suggest that the temperature suitability of Henan sesame is higher and the heat resources are rich，the light suitability is gradually decreasing and not conducive to the growth of sesame，the precipitation suitability is the lowest，and precipitation is the main climatic factor that restricts the production of sesame in Henan.In the production of Henan sesame，it is of great significance to consolidate the advantages of traditional sesame producing areas such as Zhumadian and Zhoukou in southeastern Henan，expand the planting range of high climate suitability areas such as northern and eastern Henan，improve the irrigation and drainage facilities to improve the utilization rate of sesame climate resources and ensure the stable yield and yield increase of Henan sesame. Reference | Related Articles | Metrics Select Sensitivity Analysis and Applicability Evaluation of the AquaCrop Model for Sesame Crop Parameters LI Mengyao, GUAN Haoyue, ZHANG Jiantao, HUANG Ming, LI Feng, RONG Yasi, LI Youjun, LI Guoqiang Journal of Henan Agricultural Sciences    2024, 53 (7): 149-159.   DOI: 10.15933/j.cnki.1004-3268.2024.07.017 Abstract （690）      PDF （3280KB）（159）       Knowledge map    Save The objective is to enhance the calibration efficiency of the AquaCrop model for sesame crop parameters and verify the applicability of the model. The initial value and value range of sesame crop parameters were determined based on the experimental data（2022—2023）and related literatures.The EFAST method was used to perform global sensitivity analysis of 53 sesame crop parameters，uncertainty analysis of simulation results and parameters calibration validation. The results showed that 27 parameters were sensitive to the maximum biomass of each treatment，including CDM，CDSE，POFE，RSWT，PSENSP，PSTOSP，RSWB and ECSW with TSi above 0.3.And 14 parameters were sensitive to grain yield of each treatment，including POHX，RSWT，CDSE，DMCON，PSTO and PSTOSP with TSi higher than 0.2.The decision coefficient（R2） of simulated and measured canopy coverage and aboveground biomass ranged from 0.875 to 0.954 and 0.951 to 0.970，respectively.Futhermore，the normalized root mean square error（NRMSE） ranged from 11. 5% to 18.1% and 18. 9% to 27.7%，respectively.Meanwhile，the Nash‑Sutcliffe efficiency coefficients（NSE） were 0.873—0.940 and 0.930—0.959，respectively.The relative error between the simulated and measured values of yield was between 0.03 and 0.07.The localized AquaCrop model can better simulate the dynamic development process of sesame，which can be used to optimize sesame management and forecast future production. Reference | Related Articles | Metrics