Applications of Artificial Intelligence and Hyperspectral Imaging in Smart Digital Horticulture
A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Protected Culture".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 149
Special Issue Editors
Interests: intelligent robotics system; remote sensing; smart agriculture; intelligent decision-making system
Interests: UAVs-drones; artificial intelligence; machine vision; precision agriculture
Interests: smart agriculture applications; embedded systems developing; FPGA intelligent acceleration system; information and communications technology (ICT); hyperspectral analysis
Special Issue Information
Dear Colleagues,
The burgeoning development of artificial intelligence (AI) is revolutionizing smart digital horticulture, with deep learning, computer vision, intelligent robotics, large language models (LLMs), and multi-agent systems (MAS) driving pivotal breakthroughs in plant nutrient monitoring, pest and disease diagnosis, irrigation and fertigation, biotechnology and genetic detection, yield prediction, environmental perception and control, and autonomous decision-making. Spectral analysis techniques—such as Hyperspectral Imaging (HSI), Raman spectroscopy, X-ray spectroscopy, Nuclear Magnetic Resonance (NMR) spectroscopy, Fluorescence spectroscopy, Atomic spectroscopy, Molecular spectroscopy, and Mass spectrometry—serve as powerful and effective tools for capturing high-resolution plant physiological and biochemical signatures. The integration with AI algorithms further enhances the precision of plant health evaluation, stress response characterization, nutrient dynamic analysis, crop quality assessment, and sustainable horticultural development.
This Special Issue solicits diverse AI-driven innovations applicable to horticultural scenarios, encompassing intelligent sensing systems, multimodal perception, big data analytics, digital twins, precision horticultural management models, automated horticultural machinery, and sustainable strategies for horticultural production and postharvest handling. Spectroscopy-based studies are highly encouraged but are not mandatory. The Issue aims to establish a rigorous academic platform for researchers and practitioners to disseminate cutting-edge findings, methodological advances, and real-world applications, thereby fostering the development of efficient, resilient, and sustainable horticultural systems.
Dr. Yongming Chen
Dr. Ping Lin
Dr. Zhiyong Zou
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- unmanned aerial vehicles (UAVs)
- deep learning
- computer vision
- decision-making
- horticultural crops production
- smart farming
- pest and disease detection
- yield estimation
- smart irrigation
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