Remote Sensing and Decision Support for Precision Orchard Production

Dear Colleagues,

Smart orchard is a new generation of orchard production system relying on smart sensing, smart decision, and smart intervention, targeted to optimize agronomic inputs and resilience, and improve yield/quality and production efficiency. The evolution of remote sensing techniques has opened new perspectives for supporting orchard production and management. The enhanced spectral, spatial, and temporal resolution of various sensors (i.e., multi/hyperspectral, LiDAR, thermal, and fluorescence,) on board platforms (spaceborne, airborne, UAVs, vehicle, robots, and backpack) offers unprecedented possibilities for efficient orchard monitoring at different application scales and purposes. The link of the remote sensing technology and orchard agronomic model is expected to support intelligent decisions regarding fertilizer, water, and chemical inputs, optimizing and predicting fruit yield and quality. To accelerate the transition from traditional orchard production to smart orchard, this Special Issue aims at providing the state-of-the-art of remote sensing techniques for orchard management, with a special focus on operational applications targeted to the needs of the final users, that is, fruit producers, farmer associations, and regulating authorities.

This Special Issue invites contributions on the following:

• Innovative sensors and technologies for smart orchard sensing;
• Novel intelligent data fusion methods for orchard understanding and learning;
• Orchard modelling and decision system in smart orchard application.

Submissions are encouraged to cover a broad range of topics that may include, but are not limited to, the following activities:

• Plant healthy sensor development IoT for orchard management
• Multi-platform data fusion on smart orchard
• Empirical and physical model of remote sensing
• Quantitative inversion of orchard physicochemical parameters
• Orchard structure parameters estimation
• Nutrition/water stress diagnosis
• Pest/disease monitoring and prediction orchard process-based agronomic model
• Intelligent decision-making tools
• Traceability system based on smart sensing
• Orchard yield and quality estimation
• Remote sensing and data assimilation
• Big data and systems for smart orchard
• Social–economical assessment for sensing technology

Prof. Guijun Yang
Prof. Zhenhong Li
Dr. James Taylor
Prof. Raffaele Casa
Dr. Hao Yang
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.