Edited and Reviewed by: Roger Deal, Emory University, United States
*Correspondence: Chuanlei Zhang,
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In recent years, in the field of smart agriculture, many advanced technologies and data-driven approaches have been widely applied to crop yield estimation, soil and water conservation, pest and disease detection and severity evaluation, species classification and farmland ridge segmentation, and have achieved remarkable results. This Research Topic highlights several issues that still need further research and discussion in smart agriculture, such as Agricultural UAV (AUAV), Agricultural Remote Sensing (ARS), Agricultural Internet of Things (AIoT), Agricultural Artificial Intelligence (AAI), Agriculture Digital Twin (ADT), Deep Learning and their combinations.
AUAV and ARS can capture field surface information, provide a wide range of agricultural monitoring, and help farmers and agricultural managers make better decisions. In recent years, combining AUAV, ARS and AI have been widely applied to real-time monitoring and analysis of crop growth status, crop health status and growth stage, and assessment of the impact of natural disasters, such as droughts, floods and storms, and help farmers understand the field situation and develop responses and reduce risks in time.
AIoT and MDs aim to combine AIoT and MD with AI to provide solutions for smart agriculture. Various AIoT sensors are installed in the field to collect soil moisture, temperature, humidity, crop health, weather conditions and other data in real time, and transmit the collected data to the cloud platform to achieve optimal irrigation, fertilization, disease and pest control, and accurate resource management. MDs such as smartphone and AUAV are used to capture crop images in the field to realize real-time monitoring of farmland, including crop variety classification, crop disease severity estimation and pest detection (
Digital twins have been applied to various fields, including smart agriculture. ADT is one example of many digital farming innovations. It relies on the integration of state-of-the-art agricultural technologies, including big-data analytics, AAI, AIoT, ARS, AUAV, Information & Communication Technology, Geographic Information Systems (GIS). It helps farmers monitor crop growth, soil conditions and climate change in real time, optimize the use of water, fertilizers and pesticides, reduce resource waste and environmental impact, predict potential risks, such as pests and diseases, climate change, and take steps to reduce losses, create a comprehensive basis for data-driven policy decisions, and promote sustainable development of agriculture. Recently, ADT has promoted the application of new technologies such as AIoT, AAI and big-data analytics to smart agriculture, and become an important tool for modern agricultural management. The application of ADT will revolutionize agricultural production, improving overall efficiency and sustainability.
Smart agriculture integrates advanced agricultural technologies, including AUAV, ARS, AIoT, AAI, ADT and deep learning. It can provide farmers with tools and platforms for automated irrigation, fertilization, pest control, crop pest detection, growing environment monitoring, unmanned farm management and automatic crop harvesting, so as to improve efficiency and optimize agricultural production, minimizing environmental impact and resource use. At present, there are still several open research problems that need to be further studied and perfected. Due to the complexity and diversity of agricultural big-data, as well as its large scale and universal distribution, many challenges are presented in terms of network speed, computing storage, and operations management. This topic will introduce new achievements in the fields of AUAV, ARS, AIoT, AAI, ADT, deep learning and their integration and application in sustainable smart agriculture.
SZ: Conceptualization, Funding acquisition, Writing – original draft. CZ: Validation, Writing – review & editing, Investigation, Resources. CY: Project administration, Supervision, Validation, Writing – review & editing. BL: Investigation, Supervision, Validation, Writing – review & editing.
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