AI-Integrated Smart Farming Solutions for Crop Enhancement
TL;DR
This study reviewed the state-of-the-art smart farming solutions integrated with AI, specifically aimed at crop improvement, and outlined the history of smart agriculture, examines the latest AI-driven innovations in agriculture, offers insights into ongoing research and development challenges, and recommends potential future research opportunities.
Abstract
With the speed of AI development, the agriculture sector has been revolutionized, with intelligent, data-driven farming practices enhancing productivity and sustainability while optimizing the use of resources. Smart farming with AI technology involves the integration of machine learning, deep learning, computer vision, Internet of Things (IoT), unmanned aerial vehicles (UAVs), remote sensing, and cloud computing, all working together to track crop conditions, forecast weather, manage water usage, identify plant diseases, and improve agricultural decision-making. In traditional farming, making important observations and decisions is usually done by hand and based on general knowledge, which results in waste of resources and poor crop results. AI-driven precision agriculture, on the other hand, enables farmers to track crop health in real time and make predictions that help them take proactive measures to optimise quality and reduce expenses in their operations.Precision agriculture, on the other hand, powered by AI allows farmers to monitor and predict crop health, enabling them to take proactive steps to maximise crop quality while minimising operational costs. This study reviewed the state-of-the-art smart farming solutions integrated with AI, specifically aimed at crop improvement. The paper outlines the history of smart agriculture, examines the latest AI-driven innovations in agriculture, offers insights into ongoing research and development challenges, and recommends potential future research opportunities. In addition, it highlights the importance of data analytics, sensor technologies, and autonomous agricultural systems in the context of sustainable food production in a world of growing population and climate change. Research objectives are to develop a conceptual framework based on multiple AI technologies that can be combined in a single smart farming ecosystem to boost productivity, minimize environmental impacts, increase decision making accuracy and to support sustainable agricultural development. The results have shown that smart farming with the use of artificial intelligence is a paradigm.