Intelligent Extraction of Phenotypic Traits in Agroforestry

Dear Colleagues,

This Special Issue, “Intelligent Extraction of Phenotypic Traits in Agroforestry”, aims to explore the application of remote sensing technologies in extracting phenotypic traits in agriculture and forestry. These fields are vital for sustainable development and environmental conservation, and accurately assessing phenotypic traits is crucial for optimizing resource management and enhancing productivity. Remote sensing technologies provide a powerful tool for non-destructive, large-scale monitoring, enabling the extraction of valuable phenotypic information. Advanced data science technologies, such as computer vision and deep learning, further enhance the capability of these methods. Moreover, integrating plant phenomics into genetic breeding programs can significantly improve crop yields and the selection of superior plant varieties, demonstrating the broad applications of these technologies across various agricultural and forestry disciplines.

Authors are invited to submit original research articles, reviews, and case studies that address key themes, including machine learning, deep learning, and computer vision for phenotypic trait extraction, multi-sensor data fusion techniques (e.g., LiDAR and hyperspectral imaging), and integrating remote sensing data with ground observations in agriculture and forestry. Contributions covering a wide range of topics are welcome, including, but not limited to, the following:

• Intelligent extraction of agriculture and forestry phenotypic traits using deep learning;
• High-yield plant phenotype screening using remote sensing;
• Multi-sensor data fusion for high-throughput phenotyping in agriculture and forestry;
• Application of phenotypic data in genetic breeding programs for crop improvement and sustainable forest resource management.

Dr. Xijian Fan
Dr. Yanjie Li
Dr. Shichao Jin
Dr. Cong (Vega) Xu
Dr. Sruti Das Choudhury
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-blind 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.