Intelligent Techniques for Prediction of Kharif Food Grains Yield in Gujarat| Chapter 11 | Research Highlights in Agricultural Sciences Vol. 9

 Machine learning is a lively decision support finish for crop yield prediction. Yield prognosis could be a very important land issue. Any farmer is curious about knowing what pile of yield he's getting ready for. In the past, yield guess was performed by seeing farmers' expertise in particular fields and crops. Based on previous dossier, we are able to think crop yield victimization utilizing a machine-knowledge technique. Crop yield guess is an important area of research that helps in guaranteeing food freedom all around the planet. Developing higher methods to predict crop output in several climates can assist farmers and different stakeholders in making main decisions in conditions of scientific farming and crop choice. This paper reports on the utilization of diversified linear regression (MLR), Gaussian Process, SMOreg and chance forest to think Kharif food seed crop yield for Gujrat state, India. The parameters preferred for the study were precipitation, minimum hotness, average temperature, maximum hotness, reference crop evapotranspiration, area, result, and yield for the Kharif season (June to November) for the years 2000 to 2020. The dataset was treated victimisation the WEKA tool.

Author(s) Details:

Patadiya Jaykin,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Mittal Desai,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Dip Patel,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Bhargav Vayas.,
Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT, Changa, Gujarat, India.

Please see the link here: https://stm.bookpi.org/RHAS-V9/article/view/9477

Study on Bayesian Regression Model and Applications| Chapter 8 | Novel Research Aspects in Mathematical and Computer Science Vol. 7

 We introduce a sparse vector regression model. Bayesian formulation and the Gaussian process are used to create the method. When compared to similar Bayesian vector regression models, the number of parameters in the method is minimised by applying a particular prior hyperparameter setting during the development process. The algorithm is created using an iterative computational approach. Examples of applications to the inverse scattering issue and function approximations are given.

Author(s) Details:

Yijun Yu,
Department of Mathematics, Tuskegee University, Tuskegee, AL, USA.

Please see the link here: https://stm.bookpi.org/NRAMCS-V7/article/view/7953