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Digital Innovation in Agricultural and Food Technology
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Digital Innovation in Agricultural and Food Technology

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6407

Special Issue Editors

Dr. Daniel I. Onwude

E-Mail Website
Guest Editor
Empa, Laboratory 401- Biomimetic Membranes and Textiles, ETH Domain, St. Gallen, Switzerland
Interests: computational fluid dynamic; mechanistic modeling; digital twins; drying technology; postharvest technology; agricultural engineering; food preservation and processing
Special Issues, Collections and Topics in MDPI journals
Dr. Guangnan Chen

E-Mail Website
Guest Editor
Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD, Australia
Interests: renewable energy and bio-energy; life-cycle assessment; sustainable agriculture and farming systems; crop postharvest and drying systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Worldwide, the food and agricultural sectors are currently experiencing many significant changes. Food value chains are evolving particularly rapidly, along with new consumer behaviour and expectations, data and technology, and food security and environmental sustainability challenges. Digital agriculture has the potential to vastly increase the performance of food systems and create new markets and opportunities, using digital and relevant data technologies to enable farmers and other stakeholders to optimise food production and supply systems. Digital technologies are now widely used to improve productivity and decision-making processes across all stages of food production, from genetics improvement to farm management, transport systems, and consumers. Digital food manufacturers can also improve their environmental performance in the packaging and post-harvest processes by using robotics, smart materials, and digital solutions. In this Special Issue, we invite agricultural and food researchers to contribute their original papers or review articles related to the broad topic of digital innovation in the agriculture and food industries. This may relate to development and innovation in areas such as automation technology, precision agriculture, sensing equipment, data analyses, or smart agrifood and farming systems.

Dr. Daniel I. Onwude
Dr. Guangnan Chen
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. 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

  • digital agriculture
  • precision agriculture
  • digital technologies
  • artificial intelligence and big data
  • data modelling
  • model integration
  • smart farming
  • smart agrifood
  • foods

Published Papers (2 papers)

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Research

14 pages, 4227 KiB  
Article
Development and Performance Analysis of an Automatic Core Cutter for Elephant Apple (Dillenia indica L.) Processing
by Deepanka Saikia, Radhakrishnan Kesavan, Minaxi Sharma, Baskaran Stephen Inbaraj, Prakash Kumar Nayak and Kandi Sridhar
Foods 2024, 13(6), 848; https://doi.org/10.3390/foods13060848 - 11 Mar 2024
Viewed by 861
Abstract
Elephant apple, a fruit with numerous bioactive compounds, is rich in therapeutic qualities. However, its use in processed products is limited due to insufficient postharvest processing methods. To address this issue, an automatic core cutter (ACC) was developed to handle the hard nature [...] Read more.
Elephant apple, a fruit with numerous bioactive compounds, is rich in therapeutic qualities. However, its use in processed products is limited due to insufficient postharvest processing methods. To address this issue, an automatic core cutter (ACC) was developed to handle the hard nature of the fruit while cutting. The physical characteristics of the elephant apple were considered for designing and development of the cutter. The cutter is divided into four main sections, including a frame, collecting tray, movable coring unit, and cutting base with five fruit holders. The parts that directly contact the fruit are made of food-grade stainless steel. The efficiency of the cutter was analyzed based on cutting/coring capacity, machine efficiency, loss percentage, and other factors, and was compared to traditional cutting methods (TCM) and a foot-operated core cutter (FOCC). The ACC had an average cutting/coring capacity of 270–300 kg/h, which was significantly higher than TCM’s capacity of 12–15 kg/h and comparable to FOCC’s capacity of 115–130 kg/h. The ACC offered a higher sepal yield of 85.68 ± 1.80% compared to TCM’s yield of 65.76 ± 1.35%, which was equivalent to the yield obtained by FOCC. Therefore, the ACC outperforms TCM in terms of quality, quantity, and stress associated and is superior to FOCC in terms of higher efficiency of machine and labor. Full article
(This article belongs to the Special Issue Digital Innovation in Agricultural and Food Technology)
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16 pages, 6947 KiB  
Article
Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting
by L. G. Divyanth, Peeyush Soni, Chaitanya Madhaw Pareek, Rajendra Machavaram, Mohammad Nadimi and Jitendra Paliwal
Foods 2022, 11(23), 3903; https://doi.org/10.3390/foods11233903 - 03 Dec 2022
Cited by 8 | Viewed by 4659
Abstract
Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the [...] Read more.
Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster. Full article
(This article belongs to the Special Issue Digital Innovation in Agricultural and Food Technology)
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