Plants are essential to the global food supply, yet they are increasingly vulnerable to diseases that cause significant production losses. Traditional plant disease detection methods are labor-intensive, error-prone, and insufficient for early-stage diagnosis. Recent advancements in Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), wireless communication, and Quantum-Dot Cellular Automata (QCA) nanotechnology provide innovative solutions for precise and efficient disease detection. This study introduces a novel ML- and Artificial Intelligence (AI)-based plant extract analyzing device designed to address the challenges of Indian agriculture, such as resource limitations and diverse crop varieties. The device integrates spectroscopic analysis, biochemical data interpretation, and QCA nanotechnology with cloud-enabled IoT platforms for real-time disease diagnosis. Field trials demonstrated high accuracy (97%) in identifying fungal, bacterial, and nutrient-related diseases, with superior cost efficiency (85%) and reduced data requirements (8 GB). These results underscore its potential to revolutionize agricultural practices by improving early disease detection, minimizing crop losses, and promoting sustainable farming. The findings provide insights into adopting advanced technologies in agriculture and their broader implications for food security and precision agriculture.
Plant disease detection, Machine Learning, Artificial Intelligence, IoT in agriculture, spectroscopic analysis, wireless communication, Quantum-Dot Cellular Automata, plant extract analysis
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