Neuro-Evo Swarm Optimizer (NESO): A Hybrid Framework Combining Deep Learning, Genetic Algorithms, and Particle Swarm Optimization for Multi cropping Optimization
Sustainable agricultural productivity requires intelligent decision-making systems capable of handling complex interactions among crops, resources, and environmental conditions. Multi cropping systems, though beneficial for yield stability and risk reduction, present significant optimization challenges due…
A Hybrid Intelligent Optimization Framework for Multicropping Systems Using Genetic Algorithm, Deep Learning, and Particle Swarm Optimization
Multicropping optimization is a complex and multi-objective problem that involves selecting optimal crop combinations to maximize yield and profit while minimizing resource usage. This paper proposes a hybrid intelligent optimization framework integrating Genetic Algorithm…
Intelligent Optimization of Multicropping Strategies Across Irrigation Systems using Genetic Algorithm, Deep Learning, and Particle Swarm Optimization
Agriculture plays a crucial role in ensuring food security and sustainable economic development. However, increasing population growth, limited water resources, climate variability, and inefficient crop planning practices present significant challenges to agricultural productivity. Multicropping…
Survey of Intelligent Optimization of Multicropping Strategies Across Irrigation Types Using Genetic Algorithms, Deep Learning, and Particle Swarm Optimization
This research focuses on the intelligent optimization of multicropping strategies across various irrigation systems using advanced Artificial Intelligence (AI) techniques. In regions with diverse climatic and soil conditions, selecting the optimal crop combination is…
Export this page’s citations: RIS (Zotero, Mendeley, EndNote) BibTeX