About this Research Topic
In the context of global challenges like population growth projected to exceed 10 billion by 2050-2060, and climate-related threats predicting a 2.7°C rise in Earth's average temperature by 2100, the agricultural sector stands at a critical juncture. Existing farming methods face threats from dwindling natural resources such as clean water and fertile soil, essential for food production. The urgent need for an agricultural transformation that enhances crop yields while conserving and restoring natural resources has never been more apparent. This urgent adaptation must not only increase productivity but also ensure resilience against catastrophic climate events, protect biodiversity, and promote the sustainability of ecosystems.
This Research Topic aims to explore and foster the implementation of groundbreaking technological solutions to redefine agricultural practices. Focusing on high throughput phenotyping aided by machine learning to identify stress-resistant crop genotypes represents a fundamental approach. While this technology has enhanced breeding for resilient varieties, its potential to improve yields directly for farmers remains largely untapped. Alongside, agricultural robotics and precision farming emerge as transformative technologies. Robots capable of operating autonomously or with humans can ensure continuous monitoring and management of crops, facilitating early pest and disease detection and enabling precise resource application. Furthermore, integrating machine learning for dynamic adjustment of farming inputs can drive precision agriculture to unprecedented levels of environmental sustainability.
To gather further insights in this critical field, articles that address, but are not limited to, the following topics are invited:
Smart sensor networks
Crop phenotypical and genotypical assessment
Early warning systems for crop pests and diseases
Crop life cycle monitoring systems
Cooperative harvesting and farming
Data-driven sustainability
Precision farming
Controlled Environment Agriculture
Agricultural robotics
Multimodal vision systems in agriculture
Imaging and data analysis for plant phenotyping
The green economy in agriculture
Agribusiness innovations
This Research Topic aims to explore and foster the implementation of groundbreaking technological solutions to redefine agricultural practices. Focusing on high throughput phenotyping aided by machine learning to identify stress-resistant crop genotypes represents a fundamental approach. While this technology has enhanced breeding for resilient varieties, its potential to improve yields directly for farmers remains largely untapped. Alongside, agricultural robotics and precision farming emerge as transformative technologies. Robots capable of operating autonomously or with humans can ensure continuous monitoring and management of crops, facilitating early pest and disease detection and enabling precise resource application. Furthermore, integrating machine learning for dynamic adjustment of farming inputs can drive precision agriculture to unprecedented levels of environmental sustainability.
To gather further insights in this critical field, articles that address, but are not limited to, the following topics are invited:
Smart sensor networks
Crop phenotypical and genotypical assessment
Early warning systems for crop pests and diseases
Crop life cycle monitoring systems
Cooperative harvesting and farming
Data-driven sustainability
Precision farming
Controlled Environment Agriculture
Agricultural robotics
Multimodal vision systems in agriculture
Imaging and data analysis for plant phenotyping
The green economy in agriculture
Agribusiness innovations
Keywords: High Throughput Phenotyping, Agricultural robotics, Precision farming, Crop yield estimation, Crop pest and disease detection, AI phenotyping, Plant stress phenotyping
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.
