About this Research Topic
Agriculture plays a significant role in the global economy. With the changing climate and burgeoning global population, global food systems will likely witness increasing pressures in the coming decades on supply and demand competition. Optimal utilization of limited resources while intensifying farming practices to maintain food security will be a serious challenge for future sustainable crop production. Modern agricultural operations have seen an increased use of automation, subsequently generating an enormous volume of data. Applications of data-driven technologies in agriculture have the potential to promote and maintain higher productivity, improved quality of produce, sustainability, as well as providing transparency to consumers. Recent advances in machine learning technologies and the widespread availability of high-performance computing facilities have opened new opportunities for precision agriculture, real-time monitoring and management of crop fields while minimizing the adverse effect on the environment.
The goal of this Research Topic is to disseminate novel research on the applications of automation and artificial intelligence, particularly machine learning in agriculture, promoting precision farming for easy reference to practitioners and researchers. In recent years, the agricultural sector has witnessed an increased use of sophisticated equipment such as robots, satellites, GPS, drones, and other sensor guided vehicles. These pieces of machinery serve as invaluable sources of data concerning crop growth, soil characteristics, and weather conditions. While each of these hardware systems is important on their own, the application of advanced AI and machine learning-based algorithms as accumulated data would augment the full potential of these hardware tools. Applications of AI would allow real-time monitoring and analysis of agricultural processes, generating critical knowledge to fine-tune strategies for optimal resource utilization, boosting farm productivity while minimizing environmental impact.
This Research Topic will cover the recent advances in automation and AI research in agriculture. The article collection will highlight technical successes and provide perspective on the current state of adoption and future trends. Moreover, this Topic will help identify the technological hurdles that need to be overcome for more comprehensive applications of AI in different agricultural sectors and towards improving farmers' perception. We welcome manuscripts related to crop phenotyping, insect-, disease-, weed-management, and cropping systems in general. Articles on species differentiation, identification, mapping/classification, and management of weeds and other invasive plants are highly encouraged. Sensor-based species differentiation has been a challenging task and recent technological advancements have shown promising results.
Authors are welcome to submit Original Research, Reviews, Mini Reviews, Methods, Perspectives, and Opinions covering the following topics (but not limited to) concerning automation and AI applications in the agricultural sector:
• AI and automation for monitoring crop and soil health
• AI and automation for crop phenotyping and plant breeding
• AI and automation for mapping and classification of weeds, insects, and diseases
• AI and automation for application of agrochemicals
• AI and automation for precision pest management
• Autonomous vehicles for farming
• Predictive analytics for precision farming
• Drones and computer vision for precision farming
• Farmers’ perception of automation and AI applications
• Feasibility of AI applications for resource-poor farmers
• Regulatory requirements and compliance of AI applications in agriculture
Keywords: Artificial Intelligence, machine learning, deep learning, computer vision, drones, sensors, robots, precision agriculture, precision farming, crop phenotyping, image-breed, predictive analytics, pest management
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.