Sensing and Machine Learning in Autonomous Agriculture

Dear Colleagues,

Smart agricultural technology offers opportunities to enhance agricultural production across outdoor and indoor crop cultivation systems, orchard management, non-destructive quality assessment, and safety systems. The application of state-of-the-art technologies is explored to develop solutions through AI-based and data-driven approaches in agriculture integrated with IoT. As the agricultural sector continues to advance, the safety of agricultural vehicles remains a significant concern. Furthermore, data-driven agricultural systems significantly contribute to disease detection in fruits and vegetables for quality management, automation to enhance pollination mechanisms, and insect control management in crop production across various stages, from pre-harvest to post-harvest.

Therefore, “Sensing and Machine Learning in Autonomous Agriculture” is an open Special Issue that welcomes a variety of novel scientific articles, including innovative and cutting-edge research using deep learning and data-driven systems in agricultural production systems. The Editor invites contributions of original research, review articles, and case studies to provide insights into the application of a deep learning approach to agricultural systems. The aim is to develop AI-based deep learning systems to enable intelligent digital farming in the smart agriculture space, covering a wide range of applications from outdoor to indoor production systems. This will encourage researchers to contribute to the development of new robotic platforms and data data-driven systems that can be used to address labor shortages and ensure safety while improving productivity through minimal human intervention. Furthermore, this Special Issue also invites articles focusing on the application of deep learning systems for disease detection in fruit crops, the automation of pollination mechanisms, and insect management in crop production.

Dr. Ahamed Tofael
Guest Editor

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