Automation and Digitalization in Orchard Machinery
A special issue of AgriEngineering (ISSN 2624-7402).
Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 5984
Special Issue Editors
Interests: smart/digital agriculture; artificial intelligence in agriculture; crop prediction models; UAV/UGV swarm
Special Issues, Collections and Topics in MDPI journals
Interests: sensing and automation; robotics and mechanization; Internet of things; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Tree fruit production is an essential component of the agricultural sector worldwide. Unlike field crops, complex orchard environments and unique planting patterns bring unpredictable challenges to almost every aspect of the production process. The entire industry is becoming vulnerable because it has always relied greatly on the huge amount of seasonal influx of skilled agricultural workers. This has already caused noticeable financial losses to growers due to agricultural labor shortage and increased costs. With the rapid growth of industrial manufacturing, smart/affordable sensors, computing power, and Artificial Intelligence (AI)-enabled algorithms exploring automated and digitalized orchard machinery seems to be an alternative and promising solution. Although several types of orchard machines have already been commercialized, such as mechanical hedging/pruning machines in apple orchards and green shoot thinning machines in vineyards, human involvement is still needed throughout the operations, which could be error-prone because the precision/accuracy level greatly depends on individuals’ experiences and estimations.
To address the emerging issues during the orchard production pipeline (including planting, training, thinning, pollinating, spraying, irrigating, disease monitoring, pest control, harvesting, post-harvesting, and transporting), this Special Issue aims to bring a collection of outstanding articles with the main focus on (but not limited to) the following research areas: field robotics for tree fruit crops (e.g., path planning and obstacle avoidance systems), automated machine prototypes for orchard productions, advanced in-field sensing technologies, deep learning-enabled machine vision (e.g., 3D canopy reconstruction, object detection, and semantic/instance segmentation), precision canopy management, precision crop load management; mechatronics in unmanned ground/aerial vehicles (UGVs/UAVs), self-guided platforms, automated orchard mapping systems, advanced control systems, innovations in end-effector/actuation design, and canopy–machinery interactions.
Original research articles and reviews are welcome in this Special Issue. We look forward to receiving your contributions.
Dr. Xin Zhang
Prof. Dr. Long He
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.
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Keywords
- Field robotics for tree fruit crops
- Automated machine prototypes
- Advanced sensing technologies
- Deep-learning-enabled machine vision
- Precision canopy and/or crop load management
- Mechatronics in unmanned ground/aerial vehicles (UGVs/UAVs)
- Self-guided platforms
- Automated orchard mapping systems
- Advanced control systems
- Canopy–machinery interactions
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