Applications of Deep Learning in Smart Agriculture

Dear Colleagues,

Smart agriculture, comprising precision agriculture, digital agriculture, and other new concepts in agricultural research and practice, has gained increasing attention in recent years due to the rising importance of sustainable food production and resource management, as well as to the opportunity offered by the emergence of several digital hardware and software technologies. Accordingly, the development of geospatial, information technology, Internet of Things, robotics, artificial intelligence, and data analytics applications plays an essential role in modern farm management. Traditional approaches of information and knowledge collection for the monitoring of agricultural fields is laborious, time-consuming, and may contain uncertainties. Therefore, technological advances in remote sensing platforms and sensors, digital web applications, and cloud data storage and management centers, as well as the development of intelligent data analysis methods and decision support systems, have improved the quality of monitoring of agricultural lands in order to meet agricultural requirements. Smart agriculture, based on today’s variable-rate technology, geospatial technology, sensor technology, Internet of Things, open-source data and algorithms, machine learning (e.g., deep learning), and high-performance computing can benefit from these opportunities and can address the new food production challenges related to cropping system optimization for improving productivity and reducing environmental impacts.

This is a joint Special Issue of Agronomy and Remote Sensing, titled “Applications of Deep Learning in Smart Agriculture,” that aims to present the state-of-the-art and original analytical methods based on deep learning for transforming diverse advanced agro-environmental data from machinery, drone, airborne, and satellite sensors into information relevant to various agronomy applications. Research papers that examine the latest developments in concepts, methods, techniques, and case study applications are welcomed. According to the aims and scope of these journals, articles based on the application of deep learning to agricultural remote sensing data can be submitted to Remote Sensing, while articles presenting analyses of other types of data or technologies in smart/precision agriculture can be submitted to Agronomy.

You may choose our Joint Special Issue in Agronomy.

Dr. Karem Chokmani
Dr. Yacine Bouroubi
Dr. Saeid Homayouni
Guest Editors

Manuscript Submission Information

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