A Machine Learning Assisted Optimized Iot-Based Intelligent Smart Farming Framework For Precision Agriculture And Sustainable Resource Management
DOI:
https://doi.org/10.29284/p4rw3k65Keywords:
Internet of Things (IoT), Machine Learning, Precision Agriculture, Smart Farming, CNN-LSTM, Resource Management, Sustainable Agriculture, Wireless Sensor Networks.Abstract
The rise of Internet of Things (IoT) devices and the advent of Machine Learning (ML) have revolutionized the way agriculture is done today. This paper presents level, wireless fully optimized intelligent framework that operates based on the IoT and enables the use of various types of ML algorithms to enhance precision agriculture and sustainable use of resources. A four-layered architecture was formed for this proposed architecture that includes sensor nodes at the field ommunication protocols, fog/edge computing nodes, and decision support systems in the cloud. The multi-modal data, such as Soil Moisture, Soil Temperature, Humidity, pH, Nitrogen-Phosphorus-Potassium (NPK), and precipitation are being continuously collected and analyzed by an optimized CNN-LSTM hybrid model. The classification accuracy obtained from a real-world test farm of 5 hectares using the proposed framework for crop yield prediction is at 96.2% which is 6.9% to 17.8% superior to the standalone Decision Tree, Naive Bayes, Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN). In addition, results of the deployment show 39%, 32%, 30%, 26% and 45% decrease in the water, fertilizer, pesticide, energy and lab costs respectively used in conventional farming. A test bed approach is applied to validate the framework, for which the IoT hardware consists of Raspberry Pi 4B gateways, Arduino Mega sensor nodes and communication modules such as LoRaWAN and ZigBee. The results show the viability of the framework for the realization of smart farming solutions that are scalable and data-driven, while also being resource-efficient.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
