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AI-Driven Technology for Sustainable Living
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AI-Driven Technology for Sustainable Living

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 14495

Special Issue Editor

Dr. Zongmei Gao

E-Mail Website
Guest Editor
Department of Neurology, Columbia University, New York, NY 10033, USA
Interests: data mining; machine learning; image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The quality of our daily life is co-determined by critical factors, such as the environment, infrastructure, energy, climate, etc. Emerging technologies profoundly transform our lives. As such, artificial intelligence (AI) has extensively advanced current transportation, public security, and healthcare, catalyzing the revolution of human livelihood. Within the parameters of a precisely defined scope of work, AI can leverage the intelligence of machines and robotics with deep learning capabilities to accelerate their impacts on climate and energy challenges faced by global sustainability.

This Special Issue aims to promote the development of sustainable-oriented research for accelerating innovation in critical areas related to sustainable living. We would like to invite the scientific community to submit their research related to AI-driven technologies focusing on sustainability. Contributions can include, but are not limited to, the following: climate change, sustainable public health, sustainable development, artificial intelligence, healthcare, sensors, environmental sustainability, machine learning, robotics, the Internet of Things, machine learning, and deep learning.

Dr. Zongmei Gao
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

  • climate change
  • sustainable public health
  • sustainable development
  • artificial intelligence
  • healthcare
  • sensors
  • environmental sustainability
  • machine learning
  • robotics
  • the Internet of Things
  • machine learning
  • deep learning

Published Papers (3 papers)

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Research

21 pages, 705 KiB  
Article
Artificial-Intelligence-Supported Reduction of Employees’ Workload to Increase the Company’s Performance in Today’s VUCA Environment
by Maja Rožman, Dijana Oreški and Polona Tominc
Sustainability 2023, 15(6), 5019; https://doi.org/10.3390/su15065019 - 12 Mar 2023
Cited by 8 | Viewed by 9652
Abstract
This paper aims to develop a multidimensional model of AI-supported employee workload reduction to increase company performance in today’s VUCA environment. Multidimensional constructs of the model include several aspects of artificial intelligence related to human resource management: AI-supported organizational culture, AI-supported leadership, AI-supported [...] Read more.
This paper aims to develop a multidimensional model of AI-supported employee workload reduction to increase company performance in today’s VUCA environment. Multidimensional constructs of the model include several aspects of artificial intelligence related to human resource management: AI-supported organizational culture, AI-supported leadership, AI-supported appropriate training and development of employees, employees’ perceived reduction of their workload by AI, employee engagement, and company’s performance. The main survey involved 317 medium-sized and large Slovenian companies. Structural equation modeling was used to test the hypotheses. The results show that three multidimensional constructs (AI-supported organizational culture, AI-supported leadership, and AI-supported appropriate training and development of employees) have a statistically significant positive effect on employees’ perceived reduction of their workload by AI. In addition, employees’ perceived reduced workload by AI has a statistically significant positive effect on employee engagement. The results show that employee engagement has a statistically significant positive effect on company performance. The concept of engagement is based on the fact that the development and growth of the company cannot be achieved by increasing the number of employees or by adding capital; the added value comes primarily from increased productivity, which is a result of the innovative ability of employees and their work engagement, which improve the company’s performance. The results will significantly contribute to creating new views in the field of artificial intelligence and adopting important decisions in creating working conditions for employees in today’s rapidly changing work environment. Full article
(This article belongs to the Special Issue AI-Driven Technology for Sustainable Living)
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15 pages, 36382 KiB  
Article
Reservoir Units Optimization in Pneumatic Spray Delivery-Based Fixed Spray System for Large-Scale Commercial Adaptation
by Ramesh K. Sahni, Rakesh Ranjan, Lav R. Khot, Gwen-Alyn Hoheisel and Matthew J. Grieshop
Sustainability 2022, 14(17), 10843; https://doi.org/10.3390/su141710843 - 31 Aug 2022
Cited by 6 | Viewed by 1855
Abstract
A pneumatic spray delivery (PSD)-based solid set canopy delivery system (SSCDS) consists of in-line reservoirs and micro-emitter assemblies distributed throughout perennial crop canopies. The existing PSD-based SSCDS uses a large number of reservoirs, i.e., one unit per 3 m of linear spacing, which [...] Read more.
A pneumatic spray delivery (PSD)-based solid set canopy delivery system (SSCDS) consists of in-line reservoirs and micro-emitter assemblies distributed throughout perennial crop canopies. The existing PSD-based SSCDS uses a large number of reservoirs, i.e., one unit per 3 m of linear spacing, which resulted in high installation and maintenance costs. These reservoirs also produces up to 25% post-spray chemical losses. Therefore, this study aimed to optimize the volumetric capacity and functionality of the existing reservoir for an efficient spray performance and the large-scale commercial adaptation of PSD-based SSCDS. Three reservoirs with volumetric capacities of 370 (1×), 740 (2×), and 1110 mL (3×) were developed to cover a spray span of 3.0, 6.1, and 9.1 m, respectively. Five system configurations with modified reservoirs and spray outlets were evaluated in the laboratory for pressure drop and spray uniformity. The three best system configurations were then field evaluated in a high-density apple orchard. These configurations had reservoirs with 1×, 2×, and 3× volumetric capacity and micro-emitters installed in a three-tier arrangement. Each replicate configuration was installed as a 77 m loop length encompassing 50 apple trees trained in a tall spindle architecture. A pair of water-sensitive paper (WSPs) samplers (25.4 × 25.4 mm) were placed on the abaxial and adaxial leaf surfaces in the bottom, middle, and top third of the canopy to evaluate the spray coverage (%). The PSD-based SSCDS showed no significant difference at the 5% level in terms of coverage among the three reservoir treatments. Coverage was more evenly distributed among the top, middle, and bottom zones for the 2× and 3× as compared to the 1× reservoir treatment. Overall, compared to the 1× reservoirs, the 2× and 3× reservoirs could potentially reduce the system costs by USD 20,000 and USD 23,410 ha−1, respectively, for tall spindle apple orchards and potentially reduce maintenance needs as well. Full article
(This article belongs to the Special Issue AI-Driven Technology for Sustainable Living)
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20 pages, 5059 KiB  
Article
HLNet Model and Application in Crop Leaf Diseases Identification
by Yanlei Xu, Shuolin Kong, Zongmei Gao, Qingyuan Chen, Yubin Jiao and Chenxiao Li
Sustainability 2022, 14(14), 8915; https://doi.org/10.3390/su14148915 - 21 Jul 2022
Cited by 9 | Viewed by 1783
Abstract
Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging [...] Read more.
Crop disease has been a severe issue for agriculture, causing economic loss for growers. Thus, disease identification urgently needs to be addressed, especially for precision agriculture. As of today, deep learning has been widely used for crop disease identification combined with optical imaging sensors. In this study, a lightweight convolutional neural network model is designed and validated on two publicly available imaging datasets and one self-built dataset with 28 types of leaf and leaf disease images of 6 crops as the research object. This model is an improvement of the existing convolutional neural network, reducing the floating-point operations by 65%. In addition, dilated depth-wise convolutions were used to increase the network receptive field and improve the model recognition accuracy without affecting the network computational speed. Meanwhile, two attention mechanisms are optimized to reduce attention module computation, improving the capability of the model to select the correct regions of interest. After training, this model achieved an average accuracy of 99.86%, and the image calculation speed was 0.173 s. Comparing with 11 backbone models and 5 latest crop leaf disease identification studies, the proposed model achieved the highest accuracy. Therefore, this model with an advantage of balancing between the calculation speed and recognition accuracy. Furthermore, the proposed model provides a theoretical basis and technical support for the practical application and mobile terminal applications of crop disease recognition in precision agriculture. Full article
(This article belongs to the Special Issue AI-Driven Technology for Sustainable Living)
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