Ai-Based Data-Centric Machine Learning Framework For High-Performance Iot Analytics In Precision Agriculture

Abstract

Precision farming is the future of agriculture that is being implemented in various ways and is constantly improving workers' productivity through technology. i.e. One combined to IoT devices and artificial intelligence has given the farmers a perfect platform to make their decisions based on the data. On the contrary, the agricultural sector has to deal with some serious problems like the volume and type of IoT sensor data, data quality, real-time processing, and conversion of analytics into farming insights that are easy to understand and act upon.

The paper presents a revolutionary AI-based data-oriented machine learning system constructed solely for IoT hi-tech analytics in precision farming. It is different from the traditional approaches that are model-centric since they try to focus mainly on the optimization of algorithms. On the contrary, the new AI-based data-centric machine learning system takes into account the agricultural environment's peculiarities by using methods such as effective data quality improvement, smart preprocessing, and adaptive learning mechanisms. The system consists of several parts like the smart data collection coming from the arrayed IoT sensors, the automated quality assessment and enhancement of the dataset, the creation of features of the dataset according to agricultural variables, the use of machine learning models through an ensemble approach, and the real-time support of decision-making systems.

The research paper is based on a detailed methodology that combines system design, prototype building, and laboratory testing through field tests in actual farm conditions. The new system was tested alongside traditional methods using prediction accuracy, processing efficiency, resource optimization, and practical farming outcomes as the main metrics of comparison.

As it turns out, the data-driven framework was the winner by a large margin over the traditional methods with a crop yield prediction accuracy of 94.3%, a disease detection accuracy of 89.7%, and the irrigation recommendations precision of 91.2%. The new approach resulted in water savings of 32%, fertilizer savings of 28%, and an increase in overall crop yield of 18% compared to conventional farming. The authors claim that the careful attention paid to the agricultural domain features and data quality is the main reason for the better performance of the machine learning models over the generic ones.