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AI Applications for Sustainable Fruit and Vegetable Distribution: Strategies for...
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AI Applications for Sustainable Fruit and Vegetable Distribution: Strategies for Waste Reduction

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Security and Sustainability".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1163

Special Issue Editor

Dr. Cristina L. M. Silva

E-Mail Website
Guest Editor
CBQF—Centro de Biotecnologia e Química Fina—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal
Interests: modeling and optimization of food processes; design and optimization of food process conditions; predictive microbiology and quality; assessment of quality changes in food due to processing; the formulation of new foods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue examines the role of artificial intelligence (AI) in enhancing the sustainability of fruit and vegetable distribution systems. Focusing on waste minimization, the articles included in this Special Issue investigate how AI technologies can optimize the supply chain from production to the consumer. AI forecasting tools are highlighted for their ability to align production with market demand, thereby minimizing surplus. Machine learning algorithms are applied to determine optimal picking times, improving shelf life and reducing spoilage. The use of AI in logistics is also explored, with smart algorithms developing efficient delivery routes to lower emissions and decrease transit waste.

Additionally, AI-driven sorting and grading mechanisms are assessed for their capacity to process produce rapidly and accurately, ensuring that only market-ready goods are distributed. This automation is crucial in maintaining the quality of perishable items, streamlining operations, and preventing waste. The collection emphasizes the necessity for AI systems to be designed with sustainability as a core principle, promoting a reduction in resource use and waste generation throughout the distribution chain.

The research presented provides a scientific foundation for the application of AI in food engineering, offering practical solutions for the challenges faced in the fruit and vegetable industry. It advocates for the adoption of these technologies as a means to achieve greater efficiency and sustainability in food distribution.

Dr. Cristina L. M. Silva
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. Foods 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 2900 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

  • fruits and vegetables
  • sustainable technologies
  • AI applications
  • minimizing waste
  • transformation and distribution

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Published Papers (2 papers)

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Research

18 pages, 4060 KiB  
Article
A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting
by Jiawen Li, Binfan Lin, Peixian Wang, Yanmei Chen, Xianxian Zeng, Xin Liu and Rongjun Chen
Foods 2024, 13(18), 2936; https://doi.org/10.3390/foods13182936 - 17 Sep 2024
Viewed by 273
Abstract
Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model [...] Read more.
Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model that combines RF-XGBoost is proposed in this work. It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). Then, a new feature set based on residual clustering features is generated after the hierarchical clustering is applied to classify the characteristics of the residuals. Subsequently, Extreme Gradient Boosting (XGBoost) acts as the second layer that utilizes those residual clustering features to yield the prediction results. The final prediction is by incorporating the results from the first layer and second layer correspondingly. As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R2) increase of 22% and 24%, respectively. Additionally, its generalization is validated across 42 types of agricultural products from six vegetable categories, showing its extensive practical ability. Such performances reveal that the proposed model beneficially enhances the precision of short-term agricultural product sales forecasting, with the advantages of optimizing the supply chain from producers to consumers and minimizing food waste accordingly. Full article
(This article belongs to the Special Issue AI Applications for Sustainable Fruit and Vegetable Distribution: Strategies for Waste Reduction)
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13 pages, 4572 KiB  
Article
Monocular Pose Estimation Method for Automatic Citrus Harvesting Using Semantic Segmentation and Rotating Target Detection
by Xu Xiao, Yaonan Wang, Yiming Jiang, Haotian Wu and Bing Zhou
Foods 2024, 13(14), 2208; https://doi.org/10.3390/foods13142208 - 13 Jul 2024
Viewed by 598
Abstract
The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation [...] Read more.
The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation and rotating target detection to estimate the pose of a single culture. First, Faster R-CNN is used for grab detection to identify candidate grab frames. At the same time, the semantic segmentation network extracts the contour information of the citrus fruit to be harvested. Then, the capture frame with the highest confidence is selected for each target fruit using the semantic segmentation results, and the rough angle is estimated. The network uses image-processing technology and a camera-imaging model to further segment the mask image of the fruit and its epiphyllous branches and realize the fitting of contour, fruit centroid, and fruit minimum outer rectangular frame and three-dimensional boundary frame. The positional relationship of the citrus fruit to its epiphytic branches was used to estimate the three-dimensional pose of the citrus fruit. The effectiveness of the method was verified through citrus-planting experiments, and then field picking experiments were carried out in the natural environment of orchards. The results showed that the success rate of citrus fruit recognition and positioning was 93.6%, the average attitude estimation angle error was 7.9°, and the success rate of picking was 85.1%. The average picking time is 5.6 s, indicating that the robot can effectively perform intelligent picking operations. Full article
(This article belongs to the Special Issue AI Applications for Sustainable Fruit and Vegetable Distribution: Strategies for Waste Reduction)
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