Journal ArticleDOI
Predicting the spread of postharvest disease in stored fruit, with application to apples
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TLDR
A simulation model is presented that predicts the overall incidence of disease in a bin of stored fruit as a function of initial infection levels and the fruit's susceptibility to fungal attack, and forms the basis of a predictive model that could be used by packinghouses to determine how long a given crop of fruit can be stored before the infection risk rises above a predetermined tolerable level.About:
This article is published in Postharvest Biology and Technology.The article was published on 2013-11-01. It has received 56 citations till now. The article focuses on the topics: Postharvest.read more
Citations
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Journal ArticleDOI
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
TL;DR: This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.
Journal ArticleDOI
Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks
TL;DR: The results demonstrate that the novel INAR-SSD model provides a high-performance solution for the early diagnosis of apple leaf diseases that can perform real-time detection of these diseases with higher accuracy and faster detection speed than previous methods.
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A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks.
TL;DR: The research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.
Journal ArticleDOI
Patulin in Apples and Apple-Based Food Products: The Burdens and the Mitigation Strategies.
TL;DR: This review will discuss the effects of the pathogenicity of Penicillium species, quality traits of apple cultivars, and environmental conditions on the severity of apple blue mold and patulin contamination.
Journal ArticleDOI
Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks.
TL;DR: A novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases and establishes a theoretical foundation for the application of deep learning in the field of agricultural information.
References
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Journal ArticleDOI
Biological control of postharvest diseases of fruits.
TL;DR: Biological control of postharvest diseases (BCPD) has emerged as an effective alternative to fungicide use because wound-invading necrotrophic pathogens are vulnerable to biocontrol, antagonists can be applied directly to the targeted area (fruit wounds), and a single application can significantly reduce fruit decays.
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Responses of early, mid and late season apple cultivars to postharvest application of 1-methylcyclopropene (1-MCP) under air and controlled atmosphere storage conditions
TL;DR: In this paper, the potential for commercial application of 1-methylcyclopropene (1-MCP) to maintain quality of apples under air and controlled atmosphere (CA) storage conditions was investigated.
Book
Fundamentals of the fungi
TL;DR: This book discusses fungi as Mutualistic Symbionts, Ecology and Human Affairs, and Fungi in Pathological Relationships of Agricultural Importance.
Journal ArticleDOI
The Role of Rain in Dispersal of Pathogen Inoculum
TL;DR: The importance of rain in the dispersal of micro-organisms was first demon strated in the 1880s, in the classic studies of Pierre Miquel on the microflora of the Pare Montsouris in Paris, who showed that some fungal pathogens were also dispersed by rain.
Journal ArticleDOI
Basis of predictive mycology.
TL;DR: This paper provides a short review of fungal modelling studies and suggests that some mould specificities should be taken into account when designing models for predicting germination and growth of fungi.