Innovative Machinery and Technologies Applied in Agriculture Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 4226
Special Issue Editor
Interests: heat and mass transfer; drying; rehydration; modelling; ANN; optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will focus on the innovative machinery and technologies applied in agriculture engineering. Agricultural engineering is the branch of engineering that attends to the design and exploitation of farm machinery and devices, the location and planning of farm structures, farm drainage, soil management and erosion control, water supply and irrigation, rural electrification, farm product processing and the challenge of deriving renewable energy from agricultural products. Innovative machines or novel technologies enable the solution of many of the problems that arise in agricultural engineering. Therefore, we invite the submission of papers that attend to the following areas of interest:
- Device and machine design;
- Device and machine exploitation;
- Technical diagnostics;
- System and process modeling;
- Processes optimization;
- Innovative technologies in agricultural engineering.
Prof. Dr. Górnicki Krzysztof
Guest Editor
Manuscript Submission Information
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Keywords
- agricultural engineering
- agricultural machinery
- computer simulation
- drying
- process modeling
- optimization
- technical diagnostics
- renewable energy sources
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Design and Experimentation of a Machine Vision-Based Fresh Cucumber Mass Grader
Author: Liu
Highlights: Novel Grading Mechanism: Introduces a fixed tray-type mechanism for grading North China-type cucumbers, ensuring no damage during the process.
Innovative CNN Structure: Proposes MassNet, a convolutional neural network, for efficient feature extraction and mass prediction from a single RGB view.
High-Performance Machine: Validates the cucumber grading system with a maximum capacity of 2.3t/hr, achieving 93% grading efficiency in online experiments.