2022年5期
刊物介紹
The Crop Journal (《作物學報》英文版)是中國科協主管，中國作物學會、中國農業科學院作物科學研究所和中國科技出版傳媒股份有限公司共同主辦的學術期刊。創刊于2013年。辦刊宗旨為刊載作物科學相關領域最新成果和應用技術, 開展國際學術交流, 促進我國作物科學研究水平及國際影響力的提升。主要刊登農作物遺傳育種、耕作栽培、生理生化、生態、種質資源、谷物化學、貯藏加工以及與農作物有關的生物技術、生物數學、生物物理、農業氣象等領域以第一手資料撰寫的學術論文、研究報告、簡報以及專題綜述、評述等。讀者對象是從事農作物科學研究的科技工作者、大專院校師生和具有同等水平的專業人士。中國農業科學院研究生院已將The Crop Journal列為博士研究生畢業發表論文認定期刊。 The Crop Journal與國際知名出版商Elsevier合作, 在ScienceDirect網絡出版平臺實現全文開放存取和在線預出版( 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