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Intelligent Agricultural Technologies and Corresponding Equipment
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Intelligent Agricultural Technologies and Corresponding Equipment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (10 May 2024) | Viewed by 5713

Special Issue Editor

Dr. Gang Wang

E-Mail Website
Guest Editor
College of Biological and Agricultural Engineering, Jilin University, 5988 Renmin Street, Changchun 130025, China
Interests: agricultural machinery; conservation tillage; sensors; automation; intelligence; plant protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

Agricultural productions play an important role in our daily life, and the production process costs a great deal of resources which may greatly harm the environment. For example, chemical fertilizers, pesticides and herbicides are vital for controlling crop-growing status, but chemical fertilizers may contaminate rivers and pesticides and herbicides may damage the farmers’ health. Intelligent agricultural technologies and corresponding equipment can solve the above issues to a certain degree. In terms of artificial mechanical weeding control equipment, mechanical weeding can replace chemical herbicides, mediating the burden on farmers’ health and saving the cost of herbicides. Many advantages can therefore be found in modern agricultural technologies and corresponding equipment.

The aim of this Special Issue is to introduce state-of-the-art intelligent agricultural technologies and corresponding equipment; the invention and illustration of the related mechanisms will accelerate the development of modern agricultural careers. We also aim to greatly contribute to the protection of our environment and society, so as to support sustainable development; for example, through decreasing the utilized amounts of bio-chemical solutions, mediating the labor intensities for farmers.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Applied Science, Engineering and Technology (ASET);
  2. Animal, Plant and Facility Systems (APFS);
  3. Biosystems, Biological and Ecological Engineering (BBEE);
  4. Power and Machinery Systems (PMS);
  5. Natural Resources and Environmental Systems (NRES);
  6. Information Technologies, Control Systems and Sensors (ITCSS);
  7. Renewable Energy and Material Systems (REMS);
  8. Agro-products and Food Processing Systems (AFPS);
  9. Safety, Health and Ergonomics (SHE);
  10. Emerging Science, Engineering and Technologies (ESET).

I look forward to receiving your contributions.

Dr. Gang Wang
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. Sustainability 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 2400 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

  • intelligent agricultural mechanization
  • machine vision
  • soil respiration
  • conservation tillage
  • smart agriculture

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Published Papers (3 papers)

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Research

28 pages, 2202 KiB  
Article
Social Life Cycle Assessment of Laser Weed Control System: A Case Study
by Beata Michaliszyn-Gabryś, Joachim Bronder and Janusz Krupanek
Sustainability 2024, 16(6), 2590; https://doi.org/10.3390/su16062590 - 21 Mar 2024
Cited by 2 | Viewed by 1331
Abstract
Agriculture is an important sector of the European Union in social, economic and environmental terms. To ensure the sustainability of the sector, improvements are needed in key operations. Weeding is one of the most important activities affecting farm productivity, farmer safety, food safety [...] Read more.
Agriculture is an important sector of the European Union in social, economic and environmental terms. To ensure the sustainability of the sector, improvements are needed in key operations. Weeding is one of the most important activities affecting farm productivity, farmer safety, food safety and security, and the state of the environment. New technical and organizational solutions are needed to achieve the goals of the EU policy for sustainable agriculture. One of the advanced techniques is laser-based weed control. It is important to fully understand the impact of the introduction of these techniques on markets and agricultural practices in the context of sustainability. For this reason, a social life cycle analysis (S-LCA) was carried out. The method applied in the study was based on a participatory approach. The assessment was carried out from three perspectives: the general society, the farmers and the business perspective in relation to agriculture. Expert interviews based on questionnaires and workshops were conducted to gather opinions on the impact of new laser technology on specific aspects of its implementation. The results show generally positive effects from all perspectives, especially from the farmers’ perspective. From the farmers’ point of view, the most favored factors influencing the widespread introduction of WLAT are the economic consequences, the most important of which are the production costs associated with the introduction of the new technology. According to business experts, the perspective of business development, with particular emphasis on new prospects for businesses and development, is the most important factor. The quality of life and the environment are most valued by society. Full article
(This article belongs to the Special Issue Intelligent Agricultural Technologies and Corresponding Equipment)
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19 pages, 9708 KiB  
Article
Enhancement for Greenhouse Sustainability Using Tomato Disease Image Classification System Based on Intelligent Complex Controller
by Taehyun Kim, Hansol Park, Jeonghyun Baek, Manjung Kim, Donghyeok Im, Hyoseong Park, Dongil Shin and Dongkyoo Shin
Sustainability 2023, 15(23), 16220; https://doi.org/10.3390/su152316220 - 22 Nov 2023
Cited by 1 | Viewed by 1477
Abstract
Monitoring the occurrence of plant diseases and pests such as fungi, viruses, nematodes, and insects in crops and collecting environmental information such as temperature, humidity, and light levels is crucial for sustainable greenhouse management. It is essential to control the environment through measures [...] Read more.
Monitoring the occurrence of plant diseases and pests such as fungi, viruses, nematodes, and insects in crops and collecting environmental information such as temperature, humidity, and light levels is crucial for sustainable greenhouse management. It is essential to control the environment through measures like adjusting vents, using shade nets, and employing screen controls to achieve optimal growing conditions, ensuring the sustainability of the greenhouse. In this paper, an artificial intelligence-based integrated environmental control system was developed to enhance the sustainability of the greenhouse. The system automatically acquires images of crop diseases and augments the disease image information according to environmental data, utilizing deep-learning models for classification and feedback. Specifically, the data are augmented by measuring scattered light within the greenhouse, compensating for potential losses in the images due to variations in light intensity. This augmentation addresses recognition issues stemming from data imbalances. Classifying the data is done using the Faster R-CNN model, followed by a comparison of the accuracy results. This comparison enables feedback for accurate image loss correction based on reflectance, ultimately improving recognition rates. The empirical experimental results demonstrated a 94% accuracy in classifying diseases, showcasing a high level of accuracy in real greenhouse conditions. This indicates the potential utility of employing optimal pest control strategies for greenhouse management. In contrast to the predominant direction of most existing research, which focuses on simply utilizing extensive learning and resources to enhance networks and optimize loss functions, this study demonstrated the performance improvement effects of the model by analyzing video preprocessing and augmented data based on environmental information. Through such efforts, attention should be directed towards quality improvement using information rather than relying on massive data collection and learning. This approach allows the acquisition of optimal pest control timing and methods for different types of plant diseases and pests, even in underdeveloped greenhouse environments, without the assistance of greenhouse experts, using minimal resources. The implementation of such a system will result in a reduction in labor for greenhouse management, a decrease in pesticide usage, and an improvement in productivity. Full article
(This article belongs to the Special Issue Intelligent Agricultural Technologies and Corresponding Equipment)
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17 pages, 9998 KiB  
Article
An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed
by Zhongyang Ma, Gang Wang, Jurong Yao, Dongyan Huang, Hewen Tan, Honglei Jia and Zhaobo Zou
Sustainability 2023, 15(7), 5764; https://doi.org/10.3390/su15075764 - 26 Mar 2023
Cited by 7 | Viewed by 2046
Abstract
The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately [...] Read more.
The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately and quickly. To this end, a semantic segmentation model based on an improved U-Net is proposed in this paper to address the issue of efficient and accurate identification of vegetable crops and weeds. First, the simplified visual group geometry 16 (VGG16) network is used as the coding network of the improved model, and then, the input images are continuously and naturally down-sampled using the average pooling layer to create feature maps of various sizes, and these feature maps are laterally integrated from the network into the coding network of the improved model. Then, the number of convolutional layers of the decoding network of the model is cut and the efficient channel attention (ECA) is introduced before the feature fusion of the decoding network, so that the feature maps from the jump connection in the encoding network and the up-sampled feature maps in the decoding network pass through the ECA module together before feature fusion. Finally, the study uses the obtained Chinese cabbage and weed images as a dataset to compare the improved model with the original U-Net model and the current commonly used semantic segmentation models PSPNet and DeepLab V3+. The results show that the mean intersection over union and mean pixel accuracy of the improved model increased in comparison to the original U-Net model by 1.41 and 0.72 percentage points, respectively, to 88.96% and 93.05%, and the processing time of a single image increased by 9.36 percentage points to 64.85 ms. In addition, the improved model in this paper has a more accurate segmentation effect on weeds that are close to and overlap with crops compared to the other three comparison models, which is a necessary condition for accurate spraying and accurate weeding. As a result, the improved model in this paper can offer strong technical support for the development of intelligent spraying robots and intelligent weeding robots. Full article
(This article belongs to the Special Issue Intelligent Agricultural Technologies and Corresponding Equipment)
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