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Agriculture
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Advanced Research on Information Collection, Modeling, and Control Used in...
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Advanced Research on Information Collection, Modeling, and Control Used in Facility Agriculture

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 6739

Special Issue Editors

Prof. Dr. Lihong Xu

E-Mail Website
Guest Editor
Department of Control Science & Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Interests: collection; modeling and control of new generation facility; agricultural biological environment information based on Internet of things
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanping Su

E-Mail Website
Co-Guest Editor
College of Energy and Mechanical Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
Interests: nonlinear control theory; intelligent computing and optimization; intelligent agriculture and greenhouse environment regulation

Special Issue Information

Dear Colleagues,

Greenhouse crop production is an important facility agriculture production method that can significantly improve crop yield and quality and is receiving more and more attention. Due to the inherent complexity of the greenhouse system, the regulation of greenhouse crop production is still a great challenge. Therefore, developing advanced greenhouse microclimate control technologies, system modeling methods, decision and support systems, sensors, and measurement methods is an important way to improve the economic and social efficiency of greenhouse crop production. To promote the development of greenhouse facility agriculture, this Special Issue will provide an up-to-date perspective of greenhouse system modeling and control, computer vision and image processes, artificial intelligence, Internet of Things technology in greenhouse facility agriculture, sensor and measurement techniques, and decision optimization methods. The papers that will be published in this Special Issue should serve as a useful reference for greenhouse system modeling and control studies and will contribute to further advancement of greenhouse crop production technology.

Potential topics:

  1. Wireless sensor networks and Internet of Things in agricultural greenhouses
  2. Automatic acquisition, analysis, and processing of multimodal phenotypic information of greenhouse crops
  3. Measurement technology of greenhouse crop growth state based on computer vision and image processing
  4. Intelligent modeling for crop and its environment in agricultural greenhouses
  5. Modeling and integral control of greenhouse water and fertilization systems
  6. Modeling and control of greenhouse light environment
  7. Multifactor coordinated control of greenhouse microclimate
  8. Intelligent optimal control technology of greenhouse crop production
  9. Multi-objective optimal control of crop yield and energy consumption in greenhouses
  10. Machine learning in greenhouse system modeling and control.

Prof. Dr. Lihong Xu
Dr. Yuanping Su
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. 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

  • agriculture greenhouse
  • Internet of things in greenhouse
  • greenhouse modeling for crops growth
  • greenhouse microclimate modeling
  • irrigation model for crops in greenhouse
  • machine learning in greenhouse
  • multi-factor control for greenhouse microclimate
  • greenhouse energy saving regulation
  • computer vision in greenhouse
  • automatic disease identification for crops in greenhouse
  • automated multimodal phenotyping for crops in greenhouse
  • greenhouse decision and support system

Published Papers (4 papers)

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Research

17 pages, 2930 KiB  
Article
Real-Time Parametric Path Planning Algorithm for Agricultural Machinery Kinematics Model Based on Particle Swarm Optimization
by Lihong Xu, Jiawei You and Hongliang Yuan
Agriculture 2023, 13(10), 1960; https://doi.org/10.3390/agriculture13101960 - 8 Oct 2023
Cited by 1 | Viewed by 962
Abstract
In order to meet the obstacle avoidance requirements of unmanned agricultural machinery in operation, it is necessary to plan a path to avoid obstacles in real time after obstacles are detected. However, the traditional path planning algorithm does not consider kinematic constraints, which [...] Read more.
In order to meet the obstacle avoidance requirements of unmanned agricultural machinery in operation, it is necessary to plan a path to avoid obstacles in real time after obstacles are detected. However, the traditional path planning algorithm does not consider kinematic constraints, which makes it difficult to realize the plan, thus affecting the performance of the path tracking controller. In this paper, a real-time path planning algorithm based on particle swarm optimization for an agricultural machinery parametric kinematic model is proposed. The algorithm considers the agricultural machinery kinematic model, defines the path satisfying the kinematic model through a parametric equation, and solves the initial path through the analytic method. Then, considering the constraints of obstacles, acceleration, and turning angle, two objective functions are proposed. The particle swarm optimization algorithm is used to search the path near the initial path which satisfies the obstacle avoidance condition and has a better objective function value. In addition, the influence of the algorithm parameters on the running time is analyzed, and the method of compensating the radius of the obstacle is proposed to compensate the influence of the discrete time on the obstacle collision detection. Finally, experimental results show that the algorithm can plan a path in real time that avoids any moving obstacles and has a better objective function value. Full article
(This article belongs to the Special Issue Advanced Research on Information Collection, Modeling, and Control Used in Facility Agriculture)
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17 pages, 3689 KiB  
Article
Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory
by Kaikang Chen, Bo Zhao, Liming Zhou and Yongjun Zheng
Agriculture 2023, 13(4), 888; https://doi.org/10.3390/agriculture13040888 - 17 Apr 2023
Cited by 1 | Viewed by 1392
Abstract
This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at [...] Read more.
This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage. Full article
(This article belongs to the Special Issue Advanced Research on Information Collection, Modeling, and Control Used in Facility Agriculture)
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14 pages, 3798 KiB  
Article
Performance Analysis and Selection of Chinese Solar Greenhouses in Xinjiang Desert Area
by Xiao Wu, Hong Li, Siyu Sang, Anhui He, Yimei Re and Hongjun Xu
Agriculture 2023, 13(2), 306; https://doi.org/10.3390/agriculture13020306 - 27 Jan 2023
Cited by 1 | Viewed by 1562
Abstract
This study aims to provide information and theoretical support for the development planning of facility agriculture in desert areas. Using sensor monitoring, USB cable, and computer connection record, we measured the temperature, humidity, and heat transfer distribution of ordinary brick wall greenhouse (G1), [...] Read more.
This study aims to provide information and theoretical support for the development planning of facility agriculture in desert areas. Using sensor monitoring, USB cable, and computer connection record, we measured the temperature, humidity, and heat transfer distribution of ordinary brick wall greenhouse (G1), composite wall greenhouse (G2), and assembled solar greenhouse (G3) in the Aksu desert area of Xinjiang. The results showed that G3 had the highest average temperature among the three types of greenhouses in the cold season; no difference was detected between G1 and G2 in the night temperature, while G3 has the characteristics of fast heating and cooling. On a sunny day, the heating rate of G1, G2, and G3 is 3.62, 4.4, and 4.77 °C/h, respectively. The cooling rate for G1 is 2.66 °C/h; 2.96 °C/h for G2; and 3.93 °C/h for G3. The heating rate for each greenhouse is nearly identical when it is cloudy outside, and the cooling rate of G1, G2, and G3 is 2.71, 4.2, and 4.34 °C/h, respectively. Moreover, the G3 north wall’s thermal insulation performance has clear advantages. Its wall surface can reach a temperature of 59.1 °C (G1 is 42.7 °C and G2 is 41.6 °C). This study showed that G3 possesses the virtues of effective thermal insulation; the rear wall has a small footprint and preserves the arable land; it also achieves the necessary environmental conditions for crop growth without the use of auxiliary heating. Full article
(This article belongs to the Special Issue Advanced Research on Information Collection, Modeling, and Control Used in Facility Agriculture)
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16 pages, 739 KiB  
Article
Energy-Saving Control Algorithm of Venlo Greenhouse Skylight and Wet Curtain Fan Based on Reinforcement Learning with Soft Action Mask
by Lihan Chen, Lihong Xu and Ruihua Wei
Agriculture 2023, 13(1), 141; https://doi.org/10.3390/agriculture13010141 - 5 Jan 2023
Cited by 2 | Viewed by 1887
Abstract
Due to the complex coupling of greenhouse environments, a number of challenges have been encountered in the research of automatic control in Venlo greenhouses. Most algorithms are only concerned with accuracy, yet energy-saving control is of great importance for improving economic benefits. Reinforcement [...] Read more.
Due to the complex coupling of greenhouse environments, a number of challenges have been encountered in the research of automatic control in Venlo greenhouses. Most algorithms are only concerned with accuracy, yet energy-saving control is of great importance for improving economic benefits. Reinforcement learning, as an unsupervised machine learning method with a framework similar to that of feedback control, is a powerful tool for autonomous decision making in complex environments. However, the loss of benefits and increased time cost in the exploration process make it difficult to apply it to practical scenarios. This work proposes an energy-saving control algorithm for Venlo greenhouse skylights and wet curtain fan based on Reinforcement Learning with Soft Action Mask (SAM), which establishes a trainable SAM network with artificial rules to achieve sub-optimal policy initiation, safe exploration, and efficient optimization. Experiments in a simulated Venlo greenhouse model show that the approach, which is a feasible solution encoding human knowledge to improve the reinforcement learning process, can start with a safe, sub-optimal level and effectively and efficiently achieve reductions in the energy consumption, providing a suitable environment for crops and preventing frequent operation of the facility during the control process. Full article
(This article belongs to the Special Issue Advanced Research on Information Collection, Modeling, and Control Used in Facility Agriculture)
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