Smart farming using artificial intelligence, machine learning, deep learning, and ChatGPT: Applications, opportunities, challenges, and future directions
Jayesh Rane, Ömer Kaya, Suraj Kumar Mallick, Nitin Liladhar Rane
Abstract
Using artificial intelligence (AI), machine learning (ML), deep learning (DL), and conversational models like ChatGPT, smart farming is revolutionizing the agricultural industry by increasing productivity, cutting down on resource usage, and improving decision-making. Critical agricultural problems including crop monitoring, pest identification, weather forecasting, and soil analysis can be resolved with the help of these technologies. Predictive analytics is made possible by AI and ML algorithms, which enhance crop yield by foreseeing disease outbreaks and maximizing planting schedules. With sophisticated image processing, deep learning models (DL models) enable real-time monitoring of livestock and crops, providing detailed information for precision farming. Smart farming is being further enhanced by ChatGPT and other AI-driven conversational agents. These agents offer real-time advisory services, make it possible for farmers to communicate with AI tools using natural language, and streamline difficult tasks like supply chain management, market analysis, and crop selection. Future developments in smart farming include the integration of AI with IoT devices, blockchain technology for traceability, and improved edge computing capabilities to facilitate localized, real-time decision-making.