Accelerated Cotton Leaf Disease Identification within an Advanced Robotic and Financial Intelligence Framework using Gradient-Boosted TOPSIS and Azure LLM Cloud APIs
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806809Keywords:
Accelerated cotton leaf disease identification, Machine vision, Advanced robotic intelligence, Financial decision intelligence, Transformer-based models, Gradient-Boosted TOPSIS, Multi-criteria decision-making (MCDM), LLM-powered cloud APIs, Azure Cloud, Precision agriculture, Autonomous crop monitoring, Agricultural analyticsAbstract
This study presents an accelerated and intelligent framework for cotton leaf disease identification by integrating an advanced Robotic and Financial Intelligence architecture with Gradient-Boosted TOPSIS and Azure LLM Cloud APIs. The proposed system combines machine vision–based disease detection with multi-criteria decision optimization to enhance both agricultural diagnostics and economic decision-making. A lightweight, high-performance cotton leaf disease classifier rapidly identifies major disease categories using optimized feature extraction and transformer-enhanced visual processing. The classification outputs are further evaluated using a Gradient-Boosted TOPSIS model, enabling precise prioritization of disease severity, treatment urgency, and financial impact on crop productivity. Azure LLM Cloud APIs augment the framework by providing dynamic knowledge retrieval, autonomous reasoning, and real-time recommendation generation for farmers, robotic spraying systems, and agricultural financial planners. Experimental results demonstrate improved diagnostic accuracy, reduced latency, and enhanced decision reliability compared to conventional machine-learning and TOPSIS models. This integrated, cloud-native architecture supports scalable, automated, and economically informed crop health management for precision agriculture.
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