Smart Agriculture for Sustainable Growth: Realtime Assessment of Macronutrient Analysis using IoT Sensors and Parallel Hybrid CNN_LSTM for Crop and Fertilizer Optimization
Article No.: 95, Pages 1 - 7
Abstract
Food security and sustainable growth depend on smart agriculture. Real-time macronutrient analysis using the Internet of Things (IoT) and artificial intelligence (AI) is essential for crop and fertilizer optimization because soil quality is crucial to plant growth and productivity. This study proposes a novel parallel-hybrid CNN-LSTM model with Bayesian optimization for smart agriculture soil nutrient management. Modern farming relies on resource efficiency and waste reduction. IoT technology helps farmers integrate smart farming practices for real-time monitoring and data-driven decision-making. With IoT sensors, soil data like macronutrient levels, soil temperature, and pH values are continuously collected to assess soil health. The hybrid model better captures spatial patterns and temporal dependencies in sequential data, matching soil nutrient and environmental data. Based on real-time soil nutrient data, the proposed approach identified suitable crops and fertilizers with 99.12% accuracy after rigorous experimentation and evaluation. The parallel-hybrid CNN-LSTM model and Bayesian optimization make precise and timely decisions for optimal crop growth and resource management in smart agriculture, as shown by this high accuracy rate. This strategy aims to boost agricultural productivity, sustainability, and resource efficiency to create a more resilient and eco-friendly agricultural ecosystem.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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