ArticlePlant methods2021
L2MXception: an improved Xception network for classification of peach diseases.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology.Frontiers in plant science · 2022Pooled it
- You only look once-based neural network and grading method for peach leaf shot hole disease detection under natural conditions.Frontiers in plant science · 2026Article
- Perception without preconception: comparison between the human and machine learner in recognition of tissues from histological sections.Scientific reports · 2022Article
- Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network.Frontiers in plant science · 2022Article
- DR-IIXRN : Detection Algorithm of Diabetic Retinopathy Based on Deep Ensemble Learning and Attention Mechanism.Frontiers in neuroinformatics · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundPeach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.
resultsThis paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%.
conclusionsThe proposed L2MXception network may have great potential in early identification of peach diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.