Food loss and waste in food supply chains. A systematic literature review and framework development approach

This study examines the state of the art of the literature in the domain of food loss and waste (FLW) in food supply chains (FSC). The authors used a systematic literature review (SLR) approach to examine and synthesise the findings of the existing literature to identify the key research themes, research gaps and avenues of future research on FLW in FSC. To this end, this SLR considered 152 articles relevant for the review. The authors uncovered the extant literature in the domain by presenting the research profile of the selected studies, along with thematic analysis. The authors identified eight key themes from the extant literature. The themes range from factors responsible for FLW generation to new, emerging areas of research such as digitalisation and food surplus redistribution. The study's findings will help clarify existing practices in FSC for waste mitigation and act as a foundation for strategic and policy initiatives in this area. The findings indicate that the major factors responsible for FLW include the poor management of perishable food items, stakeholder attitudes, buyer-supplier agreements and supply chain interruptions. Some of the important implications of the study include formal guidelines and policy-level interventions for assisting the accurate quantification of FLW along with an impetus on digitalisation to reduce FLW. The study concludes with the development of a research framework to assist future research in this domain. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae” crop

Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population