Alvaro Fuentes

TL;DR: A deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions, and combines each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network.

TL;DR: This study aims to address the problem of false positives and class unbalance by implementing a Refinement Filter Bank framework for Tomato Plant Diseases and Pests Recognition with an improvement of 13% compared to the previous work.

TL;DR: A simple pipeline that uses GANs in an unsupervised image translation environment to improve learning with respect to the data distribution in a plant disease dataset, reducing the partiality introduced by acute class imbalance and hence shifting the classification decision boundary towards better performance is presented.

TL;DR: Qualitative and quantitative evaluation evidence the performance of the framework as an effective method to monitor cattle behavior as well as new approach for hierarchical cattle behavior recognition with spatio-temporal information based on deep learning.

TL;DR: A comprehensive survey of image augmentation for deep learning using a novel informative taxonomy is presented in this article , where the algorithms are classified into three categories: model-free, model-based, and optimizing policy-based.