Hyperspectral Imaging and Machine Learning in Agriculture: Advances in Crop Monitoring, Phenotyping, and Decision Support

Dear Colleagues, 

Agriculture is increasingly challenged by climate variability, water scarcity, nutrient imbalance, biotic and abiotic stresses, labor limitations, and the need to produce high-quality crops with greater resource-use efficiency. In this context, hyperspectral imaging has emerged as a powerful non-destructive sensing technology for monitoring plant health, crop growth, nutrient status, stress responses, disease development, quality traits, and yield-related parameters. When combined with machine learning and deep learning approaches, hyperspectral data can provide valuable insights into plant physiological and biochemical processes and support data-driven decision-making in agricultural systems.

Recent advances in imaging sensors, proximal and remote sensing platforms, computational modeling, and artificial intelligence have expanded the potential of hyperspectral imaging across field crops, horticultural crops, controlled-environment agriculture, greenhouse systems, precision irrigation, plant phenotyping, soil and nutrient management, and post-harvest quality assessment. However, challenges remain in data preprocessing, model transferability, calibration and validation, feature selection, multimodal data fusion, interpretation of spectral signatures, and translation of predictive models into practical decision-support tools for growers and stakeholders.

We are pleased to invite you to contribute to this Special Issue, “Hyperspectral Imaging and Machine Learning in Agriculture: Advances in Crop Monitoring, Phenotyping, and Decision Support”. This Special Issue aims to collect high-quality original research articles and review papers that advance the use of hyperspectral imaging, machine learning, deep learning, and related data-driven approaches for agricultural monitoring, crop management, plant phenotyping, stress detection, and decision-support applications.

This Special Issue is intended to highlight recent developments in the integration of hyperspectral sensing with artificial intelligence to improve agricultural productivity, sustainability, resilience, and quality. Contributions may focus on fundamental methodological advances, applied agricultural case studies, crop-specific applications, sensor and platform development, model optimization, or the integration of hyperspectral data with other data sources such as RGB, RGB-D, thermal, LiDAR, fluorescence, environmental sensors, soil data, genomic/phenomic datasets, and management records.

In this Special Issue, original research articles, review papers, short communications, and perspective articles are welcome. Research areas may include, but are not limited to, the following:

• Hyperspectral imaging for crop monitoring and plant phenotyping
• Machine learning and deep learning models for agricultural spectral data
• Non-destructive prediction of nutrient status, growth, yield, and quality traits
• Detection of abiotic stresses, including drought, salinity, heat, and nutrient deficiency
• Detection and monitoring of plant diseases and pest-related stress
• Hyperspectral imaging in horticultural crops, field crops, greenhouse crops, and controlled-environment agriculture
• Integration of hyperspectral imaging with RGB, RGB-D, thermal, fluorescence, LiDAR, UAV, satellite, or proximal sensing data
• Data fusion, feature selection, spectral preprocessing, calibration, and model validation
• AI-based decision-support systems for irrigation, fertilization, crop protection, and harvest management
• Applications of hyperspectral imaging in postharvest quality, food quality, and agricultural product assessment

We look forward to receiving your contributions.

Dr. Awais Ali
Dr. Yibin Wang
Dr. Yaqoob Majeed
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.