Results 121 to 130 of about 37,961 (191)

Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. [PDF]

Heliyon
Jabed MA, Azmi Murad MA.
europepmc   +1 more source

Fertilizer Demand for Biofuel and Cereal crop Production in the United States

The emergence of biofuel production has impacted almost all sectors of the agricultural industry and the general economy and has produced a large body of research into how increased production of biofuels will impact the agricultural sector and the ...
Acheampong, Kwame, Dicks, Michael R.
core  

PRODUCTION PRACTICE ALTERNATIVES FOR INCOME AND SUITABLE FIELD DAY RISK MANAGEMENT

Production risk includes yield and days suitable for fieldwork variability. Both were modeled using biophysical simulation and a mean-variance, chance-constrained mathematical programming formulation representing a Kentucky corn, soybean, and wheat ...
Dillon, Carl R.
core  

Potential Biomass Yields in the South Central US

This paper describes research to create a national database of switchgrass supply as part of the biomass program launched by the Department of Energy. The paper provides an estimate of county level switchgrass yield data for the state of Oklahoma.
Depona, Theo, Basnet, Arjun, Hedges, Wesley, Dicks, Michael R.   +3 more
core  

Location Based Crop Yield Prediction

International Research Journal of Modernization in Engineering Technology and Science, 2023
openaire   +1 more source

Non-destructive yield estimation of onion and garlic using UAV-based hyperspectral imaging and hybrid machine learning models. [PDF]

BMC Plant Biol
Su Y, Long J, Chen J, Zhou R, Liu Z, Deng H, Yuan Y.   +6 more
europepmc   +1 more source

OPTIMIZING CROP YIELD PREDICTION CROP YIELD PREDICTION : A HYBRID APPROACH INTEGRATING CNN AND LSTM NETWORKS

Artificial Intelligence (AI) has proven successful in revolutionizing the agricultural sector, facilitating advancements in prediction, decision-making, and the monitoring and analysis of crops and soil. In this study, a hybrid model is introduced with
Jane Yan, Khoo, Wee, Bui Lin, Farashazillah, Yahya, Wang, Yin Chai, Wang, Hui Hui   +4 more
core  

Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies. [PDF]

Theor Appl Genet
Aggarwal N, Rathore M, Jan F, Sharma D, Kumar S, Thudi M, Jighly A, Varshney RK, Mir RR.   +8 more
europepmc   +1 more source

Application of deep learning in crop research: From genomics to phenomics. [PDF]

Plant Genome
Wu Z, Sun Y, Luo Q, Wei J, Cui J, Fang Y, Niu Y, Li Z, Wang X, Liu Z.   +9 more
europepmc   +1 more source

Omics-driven plant breeding through phenomics-enviromics crosstalk. [PDF]

Nat Commun
Li H, Gao S, Gebrewahid TW, Li WX, Wang J, Han Z, Reynolds MP, Araus JL, Hearne S, Xu Y.   +9 more
europepmc   +1 more source