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Machine Learning Based Smart Agriculture System


Shivappa M. Metagar, Gyanappa A. Walikar
Pages: 29-44
ISBN: 978-93-5834-410-3


Recent Research Trends in Computer Science (Volume -4)

Recent Research Trends in Computer Science
(Volume - 4)

Abstract

Traditional agriculture faces several challenges including Resource Inefficiency, it may contribute soil degradation such as overuse of fertilizers. Market Access Challenges-Small-scale traditional farmers may encounter difficulties in accessing broader markets, limiting their potential for economic growth and financial stability. Digital agriculture, a transformative paradigm integrating cutting-edge technologies such as precision farming, IoT devices, and data analytics, is revolutionizing traditional agricultural practices. Digital agriculture integrates advanced technologies, including Yield Forecast, Shopping Cart for fertilizer and pesticides and advanced tools for rapid and accurate Crop Disease Detection to revolutionize traditional farming practices. The idea of "connecting the unconnected" in this context refers to bringing technology to farmers who might not have access to advanced tools and information. In simpler terms, it's about using digital solutions like smart phones, sensors, and the internet to help farmers who may not be well-connected to the latest agricultural advancements. This connectivity allows them to access valuable information about weather patterns, soil health, crop management, market prices, and other important data. By bridging this technological gap, we aim to empower farmers with tools that can improve their productivity, reduce risks, and ultimately contribute to a more efficient and sustainable agricultural system.

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