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Found 10 papers, 0 papers with code
Pig aggression classification using CNN, Transformers and Recurrent Networks
Thus, the development of applications can assist breeders in making decisions to improve production performance and reduce costs, once the animal behavior is analyzed by humans and this can lead to susceptible errors and time consumption.
Using Deep Learning for Morphological Classification in Pigs with a Focus on Sanitary Monitoring
The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional Neural Networks) algorithms to classify pig body conditions in normal or not normal conditions, with a focus on characteristics that are observed in sanitary monitoring, and were used six different algorithms to do this task.
Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction
Smart indoor tourist attractions, such as smart museums and aquariums, usually require a significant investment in indoor localization devices.
Aedes aegypti Egg Counting with Neural Networks for Object Detection
Aedes aegypti is still one of the main concerns when it comes to disease vectors.
Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution
Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding.
A New Machine Learning Dataset of Bulldog Nostril Images for Stenosis Degree Classification
For this evaluation, the problem was modeled in two different ways: first, as a three-class classification problem (mild or open, moderate, and severe); second, as a binary classification problem, with severe stenosis as target.
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
After the backbone feature extraction, two feature maps are learned for each task.
A Deep Learning Approach Based on Graphs to Detect Plantation Lines
This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants.
A Review on Deep Learning in UAV Remote Sensing
In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields.
BioLeaf: a professional mobile application to measure foliar damage caused by insect herbivory
With these results, our proposal might assist soybean producers, reducing the time to measure foliar damage, reducing analytical costs, and defining a commodity application that is applicable not only to soy, but also to different crops such as cotton, bean, potato, coffee, and vegetables.
