A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhong...A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhongdan 909(ZD909) as tolerant hybrid to crowding stress, Jidan 209(JD209) and Neidan 4(ND4) as intolerant hybrids to crowding stress) in Northeast China. Plant densities of 4.50×104(D1), 6.75×104(D2), 9.00×104(D3), 11.25×104(D4), and 13.50×104(D5) plants ha-1had no significant effects on initial time of tassel and ear differentiation of maize. Instead, higher plant density delayed the tassel and ear development during floret differentiation and sexual organ formation stage, subsequently resulting in ASI increments at the rate of 1.2–2.9 days on average for ZD909 in 2013–2014, 0.7–4.2 days for JD209 in 2013, and 0.5–3.7 days for ND4 in 2014, respectively, under the treatments of D2, D3, D4, and D5 compared to that under the D1 treatment. Total florets, silking florets, and silking rates of ear showed slightly decrease trends with the plant density increasing, whereas the normal kernels seriously decreased at the rate of 11.0–44.9% on average for ZD909 in 2013–2014, 2.0–32.6% for JD209 in 2013, and 9.7–28.3% for ND4 in 2014 with the plant density increased compared to that under the D1 treatment due to increased florets abortive rates. It was also observed that 100-kernel weight of ZD909 showed less decrease trend compared that of JD209 and ND4 along with the plant densities increase. As a consequence, ZD909 gained its highest grain yield by 13.7 t ha-1on average at the plant density of 9.00×104 plants ha-1, whereas JD209 and ND4 reached their highest grain yields by 11.7 and 10.2 t ha-1at the plant density of 6.75×104 plants ha-1, respectively. Our experiment demonstrated that hybrids with lower ASI, higher kernel number potential per ear, and relative constant 100-kernel weight(e.g., ZD909) could achieve higher yield under dense planting in high latitude area(e.g., Northeast China展开更多
针对河西绿洲灌区水资源短缺、玉米田氮肥施用量高等生产生态问题,在节水减氮条件下,分析增加种植密度补偿水氮减量导致玉米减产的效应,为水氮节约型玉米高效生产提供理论依据与技术支撑。基于2016年布设的裂裂区田间试验,主区为2种灌...针对河西绿洲灌区水资源短缺、玉米田氮肥施用量高等生产生态问题,在节水减氮条件下,分析增加种植密度补偿水氮减量导致玉米减产的效应,为水氮节约型玉米高效生产提供理论依据与技术支撑。基于2016年布设的裂裂区田间试验,主区为2种灌水定额:灌水减量20%(W1,3240 m^(3) hm^(–2))和传统灌水(W2,4050 m^(3) hm^(–2)),裂区为2种施氮量:减量施氮25%(N1,270 kg hm^(–2))和传统施氮(N2,360 kg hm^(–2)),裂裂区为3种玉米密度:传统种植密度(D1,7.50万株hm^(–2))、增密30%(D2,9.75万株hm^(–2))和增密60%(D3,12.00万株hm^(–2)),通过测定2020—2021年玉米籽粒产量和生物产量,分析干物质积累及其分配、转运特征,量化产量构成因素,明确增密对水氮减量玉米产量的补偿效应及机制。研究表明,减水、减氮降低了玉米籽粒产量和生物产量,而增密30%能够补偿因水氮同步减量造成的产量负效应,且维持较高的施氮量有利于玉米增产节水。W1N1D1(减量灌水减量施氮及传统密度)较W2N2D1(对照:传统灌水传统施氮及传统密度)籽粒产量和生物产量分别降低9.1%~15.0%与10.0%~11.0%,但W1N1D2(减量灌水减量施氮及增密30%)与W2N2D1差异不显著。W1N2D2(减量灌水传统施氮及增密30%)较W2N2D1籽粒和生物产量分别提高12.9%~15.4%与6.4%~12.0%。增密30%能够补偿水氮同步减量造成玉米减产的主要原因是W1N1D2能增加玉米穗数,进而提高玉米灌浆初期至成熟期干物质积累量和苗期到大喇叭口期群体生长速率及花前转运率。增密30%在灌水减量和传统施氮条件下促进玉米增产的主要原因是W1N2D2可增加玉米穗数,提高玉米生育期干物质积累量与群体生长速率,促进穗部干物质分配,提高花前转运量、转运率及转运贡献率。因此,增密30%是绿洲灌区水氮同步减量玉米稳产高产的可行措施,是氮肥不减但减水20%玉米节水增产有效举措。展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is展开更多
The high nitrogen(N)application rates typically used in Chinese cropping systems have led to diminishing returns for yields and have also imposed substantial environmental costs.Here,we estimate that the annual N loss...The high nitrogen(N)application rates typically used in Chinese cropping systems have led to diminishing returns for yields and have also imposed substantial environmental costs.Here,we estimate that the annual N loss from rice production in China reached approximately 2.6×109 kg from 2011 to 2015,and we demonstrate that adoption of the mechanically dense transplanting technique by producers is an effective method to reduce N loss from rice cropping systems without suffering a yield penalty.展开更多
基金supported by the National Basic Research Program of China (2015CB150404)the National Natural Science Foundation of China (31671642)+1 种基金the Key Program of Science and Technology Department of Jilin Province, China (LFGC14205)the Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-XTCX2016008)
文摘A two-year field experiment was conducted to evaluate the effects of plant density on tassel and ear differentiation, anthesissilking interval(ASI), and grain yield formation of two types of modern maize hybrids(Zhongdan 909(ZD909) as tolerant hybrid to crowding stress, Jidan 209(JD209) and Neidan 4(ND4) as intolerant hybrids to crowding stress) in Northeast China. Plant densities of 4.50×104(D1), 6.75×104(D2), 9.00×104(D3), 11.25×104(D4), and 13.50×104(D5) plants ha-1had no significant effects on initial time of tassel and ear differentiation of maize. Instead, higher plant density delayed the tassel and ear development during floret differentiation and sexual organ formation stage, subsequently resulting in ASI increments at the rate of 1.2–2.9 days on average for ZD909 in 2013–2014, 0.7–4.2 days for JD209 in 2013, and 0.5–3.7 days for ND4 in 2014, respectively, under the treatments of D2, D3, D4, and D5 compared to that under the D1 treatment. Total florets, silking florets, and silking rates of ear showed slightly decrease trends with the plant density increasing, whereas the normal kernels seriously decreased at the rate of 11.0–44.9% on average for ZD909 in 2013–2014, 2.0–32.6% for JD209 in 2013, and 9.7–28.3% for ND4 in 2014 with the plant density increased compared to that under the D1 treatment due to increased florets abortive rates. It was also observed that 100-kernel weight of ZD909 showed less decrease trend compared that of JD209 and ND4 along with the plant densities increase. As a consequence, ZD909 gained its highest grain yield by 13.7 t ha-1on average at the plant density of 9.00×104 plants ha-1, whereas JD209 and ND4 reached their highest grain yields by 11.7 and 10.2 t ha-1at the plant density of 6.75×104 plants ha-1, respectively. Our experiment demonstrated that hybrids with lower ASI, higher kernel number potential per ear, and relative constant 100-kernel weight(e.g., ZD909) could achieve higher yield under dense planting in high latitude area(e.g., Northeast China
文摘针对河西绿洲灌区水资源短缺、玉米田氮肥施用量高等生产生态问题,在节水减氮条件下,分析增加种植密度补偿水氮减量导致玉米减产的效应,为水氮节约型玉米高效生产提供理论依据与技术支撑。基于2016年布设的裂裂区田间试验,主区为2种灌水定额:灌水减量20%(W1,3240 m^(3) hm^(–2))和传统灌水(W2,4050 m^(3) hm^(–2)),裂区为2种施氮量:减量施氮25%(N1,270 kg hm^(–2))和传统施氮(N2,360 kg hm^(–2)),裂裂区为3种玉米密度:传统种植密度(D1,7.50万株hm^(–2))、增密30%(D2,9.75万株hm^(–2))和增密60%(D3,12.00万株hm^(–2)),通过测定2020—2021年玉米籽粒产量和生物产量,分析干物质积累及其分配、转运特征,量化产量构成因素,明确增密对水氮减量玉米产量的补偿效应及机制。研究表明,减水、减氮降低了玉米籽粒产量和生物产量,而增密30%能够补偿因水氮同步减量造成的产量负效应,且维持较高的施氮量有利于玉米增产节水。W1N1D1(减量灌水减量施氮及传统密度)较W2N2D1(对照:传统灌水传统施氮及传统密度)籽粒产量和生物产量分别降低9.1%~15.0%与10.0%~11.0%,但W1N1D2(减量灌水减量施氮及增密30%)与W2N2D1差异不显著。W1N2D2(减量灌水传统施氮及增密30%)较W2N2D1籽粒和生物产量分别提高12.9%~15.4%与6.4%~12.0%。增密30%能够补偿水氮同步减量造成玉米减产的主要原因是W1N1D2能增加玉米穗数,进而提高玉米灌浆初期至成熟期干物质积累量和苗期到大喇叭口期群体生长速率及花前转运率。增密30%在灌水减量和传统施氮条件下促进玉米增产的主要原因是W1N2D2可增加玉米穗数,提高玉米生育期干物质积累量与群体生长速率,促进穗部干物质分配,提高花前转运量、转运率及转运贡献率。因此,增密30%是绿洲灌区水氮同步减量玉米稳产高产的可行措施,是氮肥不减但减水20%玉米节水增产有效举措。
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is
基金This work was supported by the National R&D Program of China(2017YFD0301503)the earmarked fund for China Agriculture Research System(CARS-O1).
文摘The high nitrogen(N)application rates typically used in Chinese cropping systems have led to diminishing returns for yields and have also imposed substantial environmental costs.Here,we estimate that the annual N loss from rice production in China reached approximately 2.6×109 kg from 2011 to 2015,and we demonstrate that adoption of the mechanically dense transplanting technique by producers is an effective method to reduce N loss from rice cropping systems without suffering a yield penalty.
