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Agriculture
Special Issues
Intelligent Agricultural Machinery Design for Smart Farming
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Intelligent Agricultural Machinery Design for Smart Farming

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 1415

Special Issue Editor

Prof. Dr. Changyou Li

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent agricultural machinery; intelligent measurement and control algorithm; drying equipment; remoting monitor of agricultural machinery; analytical theory of dryingsing technology

Special Issue Information

Dear Colleagues,

Intelligent agricultural machinery design serves as a cornerstone of smart agriculture, with the primary aim of leveraging cutting-edge technology and automation systems to optimize the efficiency and quality of agricultural production. The innovation encompasses a range of agricultural machinery and equipment, including tractors, planters, harvesters, and irrigation systems. By seamlessly integrating sensors, artificial intelligence, data analytics, and communication technology, smart agricultural machinery facilitates intelligent monitoring and automated control of field operations.

The central objective of smart agricultural machinery design is to enhance the efficiency and sustainability of agricultural production. Through real-time monitoring and analysis of critical data such as soil moisture levels, crop growth conditions, and weather patterns, smart agricultural machinery can dynamically adjust field management activities such as planting, irrigation, and fertilization to align with actual conditions. This adaptive approach maximizes crop yields while minimizing resource wastage.

Moreover, smart agricultural machinery design contributes to improved working conditions for agricultural laborers and reduces their overall workload. Automation systems replace traditional manual tasks, resulting in reduced labor requirements, enhanced operational efficiency, and a decreased risk of human error. Consequently, agricultural production becomes safer and more reliable.

This Special Issue of Agriculture welcomes novel works regarding the use of intelligent agricultural machinery for smart farming, without any restrictions on their applications.

Prof. Dr. Changyou Li
Guest Editor

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. Agriculture 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 2600 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.

Keywords

  • smart agriculture
  • drying technology
  • smart farm machinery
  • modern agriculture
  • post harvesting process

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Published Papers (1 paper)

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Research

16 pages, 5667 KiB  
Article
Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR
by Chao Zhang, Qingling Li, Shaobo Ye, Jianlong Zhang and Decong Zheng
Agriculture 2024, 14(8), 1293; https://doi.org/10.3390/agriculture14081293 - 5 Aug 2024
Viewed by 741
Abstract
During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is [...] Read more.
During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery Design for Smart Farming)
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