Co-PI Name & Affiliations:
Dr. Prabhu Jayagopal,
Associate Professor Grade 2,
School of Technology and Engineering (SITE),
Vellore Institute of Technology, Vellore, Tamilnadu, India.
Dr. Shakeel Ahmed,
College of Computer Sciences and Information Technology,
King Faisal University, Kingdom of Saudi Arabia.
Dr. Uma Shankar Subramaniam,
Associate Professor in Renewable Energy Lab,
College of Engineering,
Prince Sultan University, Saudi Arabia.
Funding Agency: Scientific research Deanship, The ministry of Education, King Faisal University,Kingdom of Saudi Arabia
Scheme: Institutional Financing Track 2020
Overlay: : Riyals 30000
Duration of the Project: 8 Months
Dr. Shakeel AhmedAssistant Professor
Dr. Prabhu JayagopalAssociate Professor Grade 2
Uma Shankar SubramaniamAssociate Professor
Crop yield depends heavily on the weather. This relationship is being shaped by growing empirical research to develop the consequences of climate change on the sectors. Saudi Arabia maize production was 89 thousand tonnes in 2019, up from 84 thousand tonnes the previous year. Forecasting maize yield with some lead time can help growers plan for need and often limited human resources and assistance in strategic business decisions. The primary goal of this proposal is to demonstrate the correlation among the various weather parameters closely related to maize yield and to estimate forecasts by using principal component analysis and machine learning techniques, namely single-layer neural network (NN) and genetic random forest. Basically, the machine learning technique provides improved skills in forecasting maize yields, when compared to principal component analysis (PCA). Further, the neural network offers most of the potential skills in predicting maize yield. While analysis of one growing season it has capable of forecasting crop yields with reasonable skills, more efforts are required to estimate this approach in various fields in the regions of Saudi Arabia.