Neutrosophic Logic-Based Crop Yield Prediction and Risk Assessment using Least Squares Regression
Year: 2024
Type: article
Abstract: Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with actionable insights for decision-making. The results demonstrate that the proposed method effectively estimates crop yield damage with the ability to assess risk under varying conditions, such as extreme weather. This approach can help farmers optimize crop management strategies, minimize losses, and improve overall productivity. Future work will explore the application of this method to other crops and farming systems to expand its utility.
Institutions: GIET University, SRKR Engineering College
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDGs: Zero hunger, Climate action
Open Access status: hybrid
OpenAlex ID: W4404098766