Obstacle detection and tracking for intelligent agricultural machinery

The shift from traditional agricultural mechanization to intelligent systems represents a cornerstone of modern agriculture, spurred by the IT revolution. This study investigates how cutting-edge measurement and control technologies can elevate the efficiency and intelligence of agricultural machinery. By employing advanced algorithms like particle filtering and SO-CDKF, alongside multi-feature fusion technology, we significantly enhance the precision and responsiveness of these systems. Our findings show a 15% improvement in measurement accuracy and a 20% reduction in response times for intelligent applications, with further efficiency gains from optimizing the fusion process. This research underscores the potential of intelligent technologies to transform agriculture, offering vital insights for its future development.

show abstract

Evaluation of Core Competitiveness of New Energy Industry and Analysis of Obstacle Factors Taking Shandong Province as an Example

Zhang,

Yang,

et al. 2024

Sustainability

View full text Add to dashboard Cite

The new energy industry serves as a key driver for green growth, and unlocking its core competitiveness is essential for achieving sustainable development. This study focuses on the new energy industry in Shandong province from 2010 to 2021, constructing an evaluation system for core competitiveness across three dimensions: industrial competitive environment, industrial competitive strength, and industrial competitive potential. Utilizing the entropy weight TOPSIS method and the obstacle degree model, this paper delves into the evolving characteristics and hindering factors affecting the core competitiveness of the new energy industry in Shandong province. The findings reveal the following: (1) over the study period, the core competitiveness of the new energy industry in Shandong province underwent stages of small fluctuations, slow growth, and rapid expansion, indicating an overall upward trajectory. (2) The primary obstacle to improving core competitiveness lies in the industrial competitive environment, particularly highlighted by factors such as the number of industrial service departments, existing policies, the growth rate of investment in the new energy industry, and the number of research and development personnel in large-scale new energy enterprises.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

334 Leonard St

Brooklyn, NY 11211

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Obstacle detection and tracking for intelligent agricultural machinery

Cited by 3 publications

References 16 publications

Introduction to the special section on emerging technologies in navigation, control and sensing for agricultural robots: Computational intelligence and artificial intelligence solutions

Introduction to the special section on emerging technologies in navigation, control and sensing for agricultural robots: Computational intelligence and artificial intelligence solutions

Application and Research on Measurement and Control Technology and Intelligentization of Agricultural Machinery in the Information Age

Evaluation of Core Competitiveness of New Energy Industry and Analysis of Obstacle Factors Taking Shandong Province as an Example

Contact Info

Product

Resources

About