A novel approach using multispectral imaging for rapid development of seed pellet formulations to mitigate drought stress in alfalfa

[1]  J. Liu,et al.  Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity , 2023, Frontiers in Plant Science.

[2]  Rui Song,et al.  Multiple omics datasets reveal significant physical and physiological dormancy in alfalfa hard seeds identified by multispectral imaging analysis , 2023, The Crop Journal.

[3]  Zhe Liu,et al.  POD, CAT and SOD enzyme activity of corn kernels as affected by low plasma pretreatment , 2022, International Journal of Food Properties.

[4]  Shangang Jia,et al.  Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning , 2022, Sensors.

[5]  J. Carstensen,et al.  Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study , 2022, Scientific Reports.

[6]  Rui Song,et al.  Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis , 2021, Sensors.

[7]  P. Townsend,et al.  Hyperspectral imagery to monitor crop nutrient status within and across growing seasons , 2021 .

[8]  N. D’ascenzo,et al.  Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography , 2021, Frontiers in Plant Science.

[9]  V. Arthur,et al.  Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases , 2020, Frontiers in Plant Science.

[10]  T. Javed,et al.  Modern Seed Technology: Seed Coating Delivery Systems for Enhancing Seed and Crop Performance , 2020, Agriculture.

[11]  M. Nuzzaci,et al.  Biological investigations on the role of hydrogel formulations containing bioactive natural agents against some common phytopathogens of Phaseolus vulgaris L. and seed germination , 2020, Journal of Biological Research - Bollettino della Società Italiana di Biologia Sperimentale.

[12]  J. Carstensen,et al.  Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality , 2020, Plant Methods.

[13]  Jürgen Bajorath,et al.  Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions , 2020, Journal of Computer-Aided Molecular Design.

[14]  R. P. K. Ambrose,et al.  Starch‐based biodegradable hydrogel as seed coating for corn to improve early growth under water shortage , 2020, Journal of Applied Polymer Science.

[15]  Chenghai Yang,et al.  Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring , 2020, Remote. Sens..

[16]  Matthias Schonlau,et al.  The random forest algorithm for statistical learning , 2020, The Stata Journal: Promoting communications on statistics and Stata.

[17]  Yaoxing Liu,et al.  Physiological effects of the combined stresses of freezing-thawing, acid precipitation and deicing salt on alfalfa seedlings , 2020, BMC Plant Biology.

[18]  Bernd Bischl,et al.  mlr3: A modern object-oriented machine learning framework in R , 2019, J. Open Source Softw..

[19]  T. Mastrangelo,et al.  Multispectral imaging for quality control of laboratory‐reared Anastrepha fraterculus (Diptera: Tephritidae) pupae , 2019 .

[20]  H. Freitas,et al.  Seed Coating: A Tool for Delivering Beneficial Microbes to Agricultural Crops , 2019, Front. Plant Sci..

[21]  J. Fehmi,et al.  Review of seed pelletizing strategies for arid land restoration , 2019, Restoration Ecology.

[22]  Qiang Zhou,et al.  MYB transcription factors in alfalfa (Medicago sativa): genome-wide identification and expression analysis under abiotic stresses , 2019, PeerJ.

[23]  A. Abdallah.  Influence of Hydrogel Type and Concentration, and Water Application Rate on some Hydraulic Properties of a Sandy Soil , 2019, Alexandria Science Exchange Journal.

[24]  Étienne Belin,et al.  Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview , 2019, Sensors.

[25]  K. Shinozaki,et al.  A gene‐stacking approach to overcome the trade‐off between drought stress tolerance and growth in Arabidopsis , 2018, The Plant journal : for cell and molecular biology.

[26]  S. Singer,et al.  Molecular improvement of alfalfa for enhanced productivity and adaptability in a changing environment. , 2017, Plant, cell & environment.

[27]  Yuhong Tang,et al.  From model to crop: functional characterization of SPL8 in M. truncatula led to genetic improvement of biomass yield and abiotic stress tolerance in alfalfa , 2017, Plant biotechnology journal.

[28]  Xiaofei Wang,et al.  Super absorbent polymer seed coatings promote seed germination and seedling growth of Caragana korshinskii in drought , 2017, Journal of Zhejiang University-SCIENCE B.

[29]  Byoung-Kwan Cho,et al.  Assessment of seed quality using non-destructive measurement techniques: a review , 2016, Seed Science Research.

[30]  F. Bavec,et al.  The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification , 2016, PloS one.

[31]  Xiangfeng Wang,et al.  Machine learning for Big Data analytics in plants. , 2014, Trends in plant science.

[32]  H. Ismail,et al.  Starch-Based Hydrogels: Present Status and Applications , 2013 .

[33]  L. Nain,et al.  Characterization of multifaceted Bacillus sp. RM-2 for its use as plant growth promoting bioinoculant for crops grown in semi arid deserts , 2012 .

[34]  P. Urwin,et al.  The interaction of plant biotic and abiotic stresses: from genes to the field. , 2012, Journal of experimental botany.

[35]  E. Brummer,et al.  Applied Genetics and Genomics in Alfalfa Breeding , 2012 .

[36]  Waham Ashaier Laftah,et al.  Polymer Hydrogels: A Review , 2011 .

[37]  Nobuhiro Suzuki,et al.  Reactive oxygen species homeostasis and signalling during drought and salinity stresses. , 2010, Plant, cell & environment.

[38]  R. Khanna-Chopra,et al.  Acclimation to drought stress generates oxidative stress tolerance in drought-resistant than -susceptible wheat cultivar under field conditions , 2007 .

[39]  S. Muller,et al.  Wettability of silicone-hydrogel contact lenses in the presence of tear-film components , 2004, Current eye research.

[40]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[41]  Henry Leung,et al.  The complex backpropagation algorithm , 1991, IEEE Trans. Signal Process..

[42]  James D. Maguire,et al.  Speed of Germination—Aid In Selection And Evaluation for Seedling Emergence And Vigor1 , 1962 .

[43]  A. Maurya.  Oxidative Stress in Crop Plants , 2020 .

[44]  D. Rao,et al.  Biodegradable Nano-Hydrogels in Agricultural Farming - Alternative Source For Water Resources☆ , 2015 .

[45]  Zhijin Zhang,et al.  Analysis of Malondialdehyde, Chlorophyll Proline, Soluble Sugar, and Glutathione Content in Arabidopsis seedling , 2013 .

[46]  K. Fujimura,et al.  A system for automated seed vigour assessment , 2001 .

[47]  G. Charmet,et al.  agronomie : plant genetics and breeding Beneficial effects of Neotyphodium lolii on the growth and the water status in perennial ryegrass cultivated under nitrogen deficiency or drought stress , 2007 .

[48]  Raúl Rojas,et al.  The Backpropagation Algorithm , 1996 .