Ai-Driven Precision Agriculture An Advanced Deep Learning Models For Agricultural Development

Abstract

Rapid developments in artificial intelligence (AI) and deep learning have had a profound impact on agriculture. Precision agriculture has transformed current farming by using effective artificial intelligence and deep learning models to increase results. This AI-powered technology allows farmers to effectively monitor crops, detect diseases early, precisely predict production, and optimize resources. This research examines cutting-edge deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer-based architectures in the context of agricultural challenges, with a focus on their role in enhancing agricultural decision-making. The experimental results of this study show that AI models outperform traditional approaches in illness detection, precise yield estimation, and resource efficiency, such as water, fertilizers, and pesticides. The comparative comparison is aided with assessment metrics including accuracy, precision, recall, and F1-score, as well as a graphical representation of model performance. The findings demonstrate the potential for deep learning to boost agricultural productivity while decreasing resource waste. This study intends to provide insights on the effectiveness of AI-driven solutions in modern farming practices, as well as identify difficulties and future prospects for improving agricultural automation and productivity.