Results 111 to 120 of about 37,961 (191)

Exploring the potential role of multi-source remote sensing data during different growth stages in crop yield prediction. [PDF]

PeerJ
Gu X, Xu E, Chi Y, Zhou L, Liang Q.
europepmc   +1 more source

A hybrid SERWI ensemble model for crop yield prediction using an inverse RMSE weighting strategy. [PDF]

Sci Rep
Ravikumar A, Periyasamy V, Devi KMK, Krishnamoorthy RG, Mariavalavan OK.   +4 more
europepmc   +1 more source

Time series and spatial analysis of crop yield

Master of ScienceDepartment of StatisticsJuan DuSpace and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is
Assefa, Yared
core  

Federated learning for crop yield prediction: A comprehensive review of techniques and applications. [PDF]

MethodsX
Hiremani V, Devadas RM, Preethi, Sapna R, Sowmya T, Gujjar P, Rani NS, Bhavya KR.   +7 more
europepmc   +1 more source

Agricultural Crop Yield Prediction Using Artificial Neural Network Approach

, 2014
: By considering various situations of climatologically phenomena affecting local weather conditions in various parts of the world. These weather conditions have a direct effect on crop yield.
Dr. S, Eep V. Rode, Miss. Snehal S. Dahikar   +2 more
core  

An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction. [PDF]

MethodsX
Yenkikar A, Mishra VP, Bali M, Ara T.
europepmc   +1 more source

Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks. [PDF]

Sci Rep
Gautam V, Gani A, Pathak S, Shukla AK.
europepmc   +1 more source

AN EVALUATION OF CROP FORECAST ACCURACY FOR CORN AND SOYBEANS: USDA AND PRIVATE INFORMATION SERVICES

Using 1971-2000 data, we examine the accuracy of corn and soybean production forecasts provided by the USDA and two private services. All agencies improved their forecasts as the harvest progressed, and forecast errors across the agencies were highly ...
Egelkraut, Thorsten M., Garcia, Philip, Irwin, Scott H., Good, Darrel L.   +3 more
core  

On-device AI for climate-resilient farming with intelligent crop yield prediction using lightweight models on smart agricultural devices. [PDF]

Sci Rep
Dhanaraj RK, Maragatharajan M, Sureshkumar A, Balakannan SP.   +3 more
europepmc   +1 more source

Crop Yield Prediction Using ML

India’s agriculture sector is pivotal to the nation’s economy and sustains livelihoods for millions. With diverse agro- climatic zones, India boasts a rich agricultural heritage encom- passing crops like rice, wheat, sugarcane, and cotton.For farmers, decision-makers, and other stakeholders to allocate resources and ensure food security, accurate crop ...
openaire   +2 more sources