A5036936607- Spectroscopy and Chemometric Analyses
- Smart Agriculture and AI
- Remote-Sensing Image Classification
- Leaf Properties and Growth Measurement
- Automated Road and Building Extraction
- Video Surveillance and Tracking Methods
- Remote Sensing and LiDAR Applications
Eduardo Mondlane University
2023-2024
Universidade Federal do Paraná
2023-2024
Product quality certification is an important process in agricultural production and productivity. Traditional methods for seed classification have shown limitations such as complex steps, low precision, slow inspection large volumes. Automatic techniques based on machine learning computer vision offer fast high throughput solutions. Despite the major advances state-of-the-art automatic models, there still a need to improve these models by incorporating other techniques. In this article, we...
Tomatoes are widely cultivated, both by family farmers and corporate producers. During the tomato growth cycle, several diseases can affect plant. The identification of these through short-range images is significant, computer vision techniques commonly used to identify in plant leaves. In this paper, a hybrid model that combines convolutional neural network (CNN) Random Forest (RF) decision tree for foliar spot detection High-level features learned extracted from CNN as input RF classifier....
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The presence of noise on hyperspectral images causes degradation and hinders efficiency processing for land cover classification. In this sense, removing or detecting noisy bands automatically becomes a challenge research in remote sensing. To cope problem, an integrated model (SAE-1DCNN) is presented study, based Stacked-Autoencoders (SAE) Convolutional Neural Networks (CNN) algorithms the selection exclusion bands. proposed employs convolutional layers to improve performance autoencoders...