论文信息
论文摘要
Abstract Agriculture is one of the key sectors in the global economy and is a key component of food security, rural employment and sustainable development. But, there are certain serious problems in the modern agriculture like climate change, scarcity of water, degradation of soil, rise in production cost, labour scarcity, crop diseases, attack by pests, and food demands. The collection of periodic observations and experience of traditional farming methods may not always be sufficient and reliable information for making complex decision making in agriculture. Smart farming, with the assistance of Artificial Intelligence (AI), can be a data-driven solution to solve these challenges. With AI, one can gather and process vast amounts of data from sensors, satellites, drones, weather stations, farming equipment, and mobile devices, and then take action on that information. The use of machine learning, deep learning, computer vision, robotics, Internet of Things (IoT), predictive analytics and decision-support systems is gaining traction in agriculture. These technologies can help farmers in the selection of crops, soil testing, precision irrigation, fertilizer management, pest/disease detection, yield prediction, weed management, harvesting, livestock monitoring, and supply-chain management. This research paper reviews the significant applications of Artificial Intelligence in smart farming, its benefits, technical architecture, challenges and future prospects. The study was conducted based on the analysis of recent studies published in the literature on 6R (AI, Precision Agriculture, AIoT, Smart Irrigation, Remote Sensing, and Machine Learning). Recent systematic reviews suggest crop monitoring, irrigation, pest and disease management, yield prediction, nutrient management, robotics, and protected cultivation are key areas where AI is applicable. The paper concludes that with the proper infrastructure, farmer training, reliable data, and affordable technologies, AI can play a significant role in productive, efficient, climate-resilient, and sustainable agriculture.
论文原文与出版信息
Zenodo (CERN European Organization for Nuclear Research)