About this Research Topic
This Research Topic is part of the article collection series - Weed Identification and Integrated Control.
Crop production is significantly influenced by a variety of factors, including weeds, pests, diseases, and environmental conditions. Traditionally, pesticide application has been the primary method for plant protection. However, chemical control methods have significant drawbacks, such as crop injury, non-point source pollution, biodiversity loss, and the development of pesticide resistance. Consequently, sustainable agriculture requires integrated field management systems that encompass mechanical control, crop rotation, variable spraying, and ecosystem-based approaches.
In addition to pest management, precise identification and monitoring of crop and environmental factors are crucial for optimizing field strategies. Sensing technology plays a pivotal role in generating accurate spatial and temporal data about crops and field conditions. Various sensors integrated with UAVs or ground-based platforms are utilized to monitor plant health, detect weed infestations, assess pest and disease stresses, and evaluate soil moisture and nutrient levels in a timely manner. Artificial intelligence (AI) has revolutionized the agriculture sector by enhancing the accuracy and efficiency of data analysis, making site-specific interventions possible. AI's advanced machine learning algorithms enable the precise identification of pests, and other crop stresses using visual or spectral imaging technologies. Robotic systems and advanced field management technologies further transform labor-intensive agricultural tasks, allowing unmanned systems to perform around-the-clock operations, thereby enhancing efficiency and sustainability. These innovations collectively facilitate more precise and effective crop management strategies.
The goal of this Research Topic is to explore and present cutting-edge research on the development and implementation of technologies for precise information identification and integrated control in agriculture, including crucial aspects such as pest identification and control, crop health monitoring, and field management. We encourage advances in high-resolution sensing techniques, AI-driven data analysis, and robotic systems capable of real-time field interventions. These innovations facilitate the integration of various data sources and technology frameworks to create robust decision-support systems for farmers, significantly enhancing the efficiency and effectiveness of plant protection, crop health monitoring, and overall field management for sustainable agriculture.
We welcome submissions of all article types accepted in Frontiers in Plant Science. Specific themes of interest for this Research Topic include, but are not limited to:
• Sensor-based plant health monitoring
• Integrated field management systems
• Mechanical and robotic field interventions
• AI-driven crop and environmental data analysis
• Variable application strategies for irrigation, nutrients, and pesticides
• Sensing and monitoring of environmental factors affecting crop growth
• Decision support systems for optimized field management
Crop production is significantly influenced by a variety of factors, including weeds, pests, diseases, and environmental conditions. Traditionally, pesticide application has been the primary method for plant protection. However, chemical control methods have significant drawbacks, such as crop injury, non-point source pollution, biodiversity loss, and the development of pesticide resistance. Consequently, sustainable agriculture requires integrated field management systems that encompass mechanical control, crop rotation, variable spraying, and ecosystem-based approaches.
In addition to pest management, precise identification and monitoring of crop and environmental factors are crucial for optimizing field strategies. Sensing technology plays a pivotal role in generating accurate spatial and temporal data about crops and field conditions. Various sensors integrated with UAVs or ground-based platforms are utilized to monitor plant health, detect weed infestations, assess pest and disease stresses, and evaluate soil moisture and nutrient levels in a timely manner. Artificial intelligence (AI) has revolutionized the agriculture sector by enhancing the accuracy and efficiency of data analysis, making site-specific interventions possible. AI's advanced machine learning algorithms enable the precise identification of pests, and other crop stresses using visual or spectral imaging technologies. Robotic systems and advanced field management technologies further transform labor-intensive agricultural tasks, allowing unmanned systems to perform around-the-clock operations, thereby enhancing efficiency and sustainability. These innovations collectively facilitate more precise and effective crop management strategies.
The goal of this Research Topic is to explore and present cutting-edge research on the development and implementation of technologies for precise information identification and integrated control in agriculture, including crucial aspects such as pest identification and control, crop health monitoring, and field management. We encourage advances in high-resolution sensing techniques, AI-driven data analysis, and robotic systems capable of real-time field interventions. These innovations facilitate the integration of various data sources and technology frameworks to create robust decision-support systems for farmers, significantly enhancing the efficiency and effectiveness of plant protection, crop health monitoring, and overall field management for sustainable agriculture.
We welcome submissions of all article types accepted in Frontiers in Plant Science. Specific themes of interest for this Research Topic include, but are not limited to:
• Sensor-based plant health monitoring
• Integrated field management systems
• Mechanical and robotic field interventions
• AI-driven crop and environmental data analysis
• Variable application strategies for irrigation, nutrients, and pesticides
• Sensing and monitoring of environmental factors affecting crop growth
• Decision support systems for optimized field management
Keywords: artificial intelligence, precision agriculture, pest control, plant health monitoring, robotic field management, environmental sensing
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.
