2022年5期
刊物介绍
The Crop Journal (《作物学报》英文版)是中国科协主管，中国作物学会、中国农业科学院作物科学研究所和中国科技出版传媒股份有限公司共同主办的学术期刊。创刊于2013年。办刊宗旨为刊载作物科学相关领域最新成果和应用技术, 开展国际学术交流, 促进我国作物科学研究水平及国际影响力的提升。主要刊登农作物遗传育种、耕作栽培、生理生化、生态、种质资源、谷物化学、贮藏加工以及与农作物有关的生物技术、生物数学、生物物理、农业气象等领域以第一手资料撰写的学术论文、研究报告、简报以及专题综述、评述等。读者对象是从事农作物科学研究的科技工作者、大专院校师生和具有同等水平的专业人士。中国农业科学院研究生院已将The Crop Journal列为博士研究生毕业发表论文认定期刊。 The Crop Journal与国际知名出版商Elsevier合作, 在ScienceDirect网络出版平台实现全文开放存取和在线预出版( journals/the-crop-journal/2214-5141)。
The Crop Journal
Research Papers
- Assessing canopy nitrogen and carbon content in maize by canopy spectral reflectance and uninformative variable elimination
- Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks
- Leaf pigment retrieval using the PROSAIL model:Influence of uncertainty in prior canopy-structure information
- The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection
- Field estimation of maize plant height at jointing stage using an RGB-D camera
- Quantifying the effects of stripe rust disease on wheat canopy spectrum based on eliminating non-physiological stresses
- Estimation of spectral responses and chlorophyll based on growth stage effects explored by machine learning methods
- Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies
- Comparison of algorithms for monitoring wheat powdery mildew using multi-angular remote sensing data
- An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved Faster R-CNN
- Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing
- Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?
- Detecting winter canola (Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data
- Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum
- Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery
- Panicle-3D: A low-cost 3D-modeling method for rice panicles based on deep learning,shape from silhouette,and supervoxel clustering
- Multichannel imaging for monitoring chemical composition and germination capacity of cowpea (Vigna unguiculata) seeds during development and maturation
- SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation
- A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem
- Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification
- Function fitting for modeling seasonal normalized difference vegetation index time series and early forecasting of soybean yield
- Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network
- Integrating remotely sensed water stress factor with a crop growth model for winter wheat yield estimation in the North China Plain during 2008-2018
- Mapping rapeseed planting areas using an automatic phenology-and pixel-based algorithm (APPA) in Google Earth Engine
- Changes and determining factors of crop evapotranspiration derived from satellite-based dual crop coefficients in North China Plain
- Temporal sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series