Special Issue "Knowledge-Based Biotechnology for Food, Agriculture and Fisheries"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Jesus Simal-Gandara
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Guest Editor
Department of Analytical Chemistry and Food Science, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, E-32004 Ourense, Spain
Interests: phenolic compounds; antioxidants; marine drugs; food safety; bioaccessibility; functional foods
Special Issues and Collections in MDPI journals
Prof. Dr. Jianbo Xiao
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Guest Editor
Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, China
Interests: natural products; flavonoids; Stilbenoids; Pharmacokinetics; phytochemicals; diabetes; glycosylation; polyphenols; nutrition and metabolism; biological activity; function food; polyphenol-protein interaction
Special Issues and Collections in MDPI journals
Dr. Md Afjalus Siraj
E-Mail Website
Guest Editor
Department of Therapeutic Radiology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
Interests: cancer signaling; RNA biology; chemotherapeutic drug resistance; chemotherapeutic role of nature derived small molecules; chemopreventive role of dietary constituents

Special Issue Information

Dear Colleagues,

Renewable biological resources are the basis of a knowledge-based bioeconomy (food, feed, agriculture, forest based, fisheries, aquaculture, biochemistry, etc.). The increasing demand for biological resources, both in quantity and quality, can only be met through innovation and advancement of knowledge in the sustainable management, production and use of these biological resources (micro-organisms, plants and animals). This Special Issue brings together all relevant actors (appropriate research disciplines and industrial sectors, farmers, forest owners, consumers, etc.) to develop the basis for new, sustainable, safer, affordable, eco-efficient and competitive products. This will help increase the competitiveness of agriculture and biotechnology, seed and food companies, in particular high-tech SMEs, while improving social welfare and well-being and reducing environmental footprints.

This key research into the safety of food and feed chains, diet-related diseases, food choices and the impact of food and nutrition on health will help to fight diet-related disorders (e.g., obesity, allergies, etc.) and infectious diseases (e.g., transmissible spongiform encephalopathies, avian flu, bluetongue, etc.), while making important contributions to the implementation of existing, and the formulation of future, policies and regulations in the area of public, animal and plant health and consumer protection.

Prof. Dr. Jesus Simal-Gandara
Prof. Dr. Jianbo Xiao
Dr. Md Afjalus Siraj
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 papers will be 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. Applied Sciences 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 2000 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

  • natural waste and byproducts
  • valorization strategies
  • bioprocesses optimization
  • added-value products
  • cosmetics
  • food and feed chains
  • diet-related disorders
  • infectious diseases
  • policies in the area of public, animal and plant health

Published Papers (2 papers)

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Research

Article
Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
Appl. Sci. 2021, 11(18), 8663; https://doi.org/10.3390/app11188663 - 17 Sep 2021
Viewed by 289
Abstract
The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy [...] Read more.
The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters. Full article
(This article belongs to the Special Issue Knowledge-Based Biotechnology for Food, Agriculture and Fisheries)
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Article
On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
Appl. Sci. 2021, 11(14), 6269; https://doi.org/10.3390/app11146269 - 07 Jul 2021
Viewed by 492
Abstract
For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an [...] Read more.
For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application and promotion of apple picking robots. Since the initial detection results have an important impact on the subsequent edge prediction, this paper proposed an automatic detection method for apple fruit targets in natural environments based on saliency detection and traditional color difference methods. Combined with the original image, the histogram backprojection algorithm was used to further optimize the salient image results. A dynamic adaptive overlapping target separation algorithm was proposed to locate the single target fruit and further to determine the initial contour for DeepSnake, in view of the possible overlapping fruit regions in the saliency map. Finally, the target fruit was labeled based on the segmentation results of the examples. In the experiment, 300 training datasets were used to train the DeepSnake model, and the self-built dataset containing 1036 pictures of apples in various situations under natural environment was tested. The detection accuracy of target fruits under non-overlapping shaded fruits, overlapping fruits, shaded branches and leaves, and poor illumination conditions were 99.12%, 94.78%, 90.71%, and 94.46% respectively. The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images, which showed that the proposed algorithm can effectively separate the overlapping fruits through a not-very-large training samples and realize the rapid and accurate detection of apple targets. Full article
(This article belongs to the Special Issue Knowledge-Based Biotechnology for Food, Agriculture and Fisheries)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Phytoremediation of Trace Elements: A Sustainable Green Solution for Clean Environment
Authors: S. M. Omar Faruque Babu 1, M. Belal Hossain 2, M. Safiur Rahman 3, Moshiur Rahman 4, A. S. Shafiuddin Ahmed 5, Md. Monjurul Hasan 6, Ahmed Rakib 7, Talha Bin Emran 8, Jianbo Xiao 9 and Jesus Simal-Gandara 9
Affiliations: 1 Technical Service Division, EON group, Dhaka, Bangladesh
2 Department of Fisheries and Marine Science, Noakhali Science and Technology University, Sonapur 3814, Bangladesh
3 Atmospheric and Environmental Chemistry Laboratory, Chemistry Division, Atomic Energy Centre Dhaka (AECD), GPO Box 164, Dhaka 1000, Bangladesh
4 Department of Fisheries (DoF), Ministry of Fisheries and Livestock, Bangladesh
5 Technical Service Division, Opsonin Pharma Ltd, Dhaka, Bangladesh.
6 Bangladesh Fisheries Research Institute, Riverine Station, Chandpur, Bangladesh
7 Department of Pharmacy, Faculty of Biological Sciences, University of Chittagong, Chittagong 4331, Bangladesh
8 Department of Pharmacy, BGC Trust University Bangladesh, Chittagong 4381, Bangladesh
Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, E32004 Ourense, Spain
Abstract: Contamination of aquatic ecosystems by various sources has become a major worry all over the world. Pollutants can enter the human body through the food chain from aquatic and soil habitats. These pollutants can cause various chronic diseases in humans and mortality if they collect in the body over an extended period. Although the phytoremediation technique cannot completely remove harmful materials, it is an environmentally benign, cost-effective, and natural process that has no negative effects on the environment. The main types of phytoremediation, their mechanisms, and strategies to raise the remediation rate and the use of genetically altered plants, phytoremediation plant prospects, economics, and usable plants were reviewed in this review. Several factors influence the phytoremediation process, including types of contaminants, pollutant characteristics, and plant species selection, climate considerations, flooding and aging, the effect of salt, soil parameters, and redox potential. Phytoremediation's environmental and economic efficiency, use, and relevance are depicted in our work. Multiple recent breakthroughs in phytoremediation technologies were also mentioned in this review.
Keywords: Phytoremediation; trace elements; pollution; green environment; aquatic plant

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