The effect of augmentation and transfer learning on the modelling of lower-limb sockets using 3D adversarial autoencoders

Costa, Ana Oliveira da; Rodrigues, Daniel Nava; Castro, Marina; Assis, Sofia; Oliveira, Hélder P.

doi:10.1016/j.displa.2022.102190

Cited by 2 publications

(1 citation statement)

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“…Early disease identification can result in more successful treatments and longer survival spans. Although transfer learning has been used in several disease detection methods [15][16][17][18][19], researchers need to develop more disease detection methods in coffee plants using transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease

2022

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Coffee is the world’s most traded tropical crop, accounting for most export profits, and is a significant source of income for the countries in which it is produced. To meet the needs of the coffee market worldwide, farmers need to increase and monitor coffee production and quality. Coffee leaf disease is a significant factor that decreases coffee quality and production. In this research study, we aim to accurately classify and detect the diseases in four major types of coffee leaf disease (phoma, miner, rust, and Cercospora) in images using deep learning (DL)-based architectures, which are the most powerful artificial intelligence (AI) techniques. Specifically, we present an ensemble approach for DL models using our proposed layer. In our proposed approach, we employ transfer learning and numerous pre-trained CNN networks to extract deep characteristics from images of the coffee plant leaf. Several DL architectures then accumulate the extracted deep features. The best three models that perform well in classification are chosen and concatenated to build an ensemble architecture that is then given into classifiers to determine the outcome. Additionally, a data pre-processing and augmentation method is applied to enhance the quality and increase the data sample’s quantity to improve the training of the proposed method. According to the evaluation in this study, among all DL models, the proposed ensemble architecture outperformed other state-of-the-art neural networks by achieving 97.31% validation. An ablation study is also conducted to perform a comparative analysis of DL models in different scenarios.

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