Al-driven Precision Agriculture: Advancing Crop Yield Prediction
Al-driven Precision Agriculture: Advancing Crop Yield Prediction
Sunny Guntuka
TLDR
The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture.
Abstract
This article explores the integration of Artificial Intelligence (AI) in precision agriculture, focusing on its role in advancing crop yield prediction and improving overall agricultural productivity. It examines three key areas: satellite imagery analysis, soil health monitoring, and weather data integration. The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices. It highlights the technical implementations of these technologies, their applications, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture. The study emphasizes the crucial role of AI in addressing global food security challenges and improving agricultural resilience to climate change.