2022年5期
刊物介紹
The Crop Journal (《作物學(xué)報》英文版)是中國科協(xié)主管，中國作物學(xué)會、中國農(nóng)業(yè)科學(xué)院作物科學(xué)研究所和中國科技出版?zhèn)髅焦煞萦邢薰竟餐鬓k的學(xué)術(shù)期刊。創(chuàng)刊于2013年。辦刊宗旨為刊載作物科學(xué)相關(guān)領(lǐng)域最新成果和應(yīng)用技術(shù), 開展國際學(xué)術(shù)交流, 促進(jìn)我國作物科學(xué)研究水平及國際影響力的提升。主要刊登農(nóng)作物遺傳育種、耕作栽培、生理生化、生態(tài)、種質(zhì)資源、谷物化學(xué)、貯藏加工以及與農(nóng)作物有關(guān)的生物技術(shù)、生物數(shù)學(xué)、生物物理、農(nóng)業(yè)氣象等領(lǐng)域以第一手資料撰寫的學(xué)術(shù)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(nóng)作物科學(xué)研究的科技工作者、大專院校師生和具有同等水平的專業(yè)人士。中國農(nóng)業(yè)科學(xué)院研究生院已將The Crop Journal列為博士研究生畢業(yè)發(fā)表論文認(rèn)定期刊。 The Crop Journal與國際知名出版商Elsevier合作, 在ScienceDirect網(wǎng)絡(luò)出版平臺實現(xiàn)全文開放存取和在線預(yù)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