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Deep learning for soybean monitoring and management. Author(s): BARBEDO, J. G. A. This review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis... ... Repository: BDPA Publication year: 2023
A study on the detection of cattle in UAV images using deep learning. Author(s): BARBEDO, J. G. A.; KOENIGKAN, L. V.; SANTOS, T. T.; SANTOS, P. M. Abstract: Unmanned aerial vehicles (UAVs) are being increasingly viewed as valuable tools to aid the management of farms. This kind of technology can be particularly useful in the context of extensive... ... Repository: BDPA Publication year: 2019
Identificação de solo exposto e cupinzeiros em pastagens utilizando deep learning. Pasture degradation is a significant challenge in livestock farming in Brazil, affecting the environmental and economic sustainability of the sector. Solutions that help manage pasture areas are cruci... ... Repository: BDPA Publication year: 2024
Detecting aquaculture with deep learning in a low-data setting. Aquaculture is growing rapidly in the Amazon basin and detailed spatial information is needed to understand the trade-offs between food production, economic development, and environmental impacts. Lar... ... Repository: BDPA Publication year: 2023
Mapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning. Author(s): ANTUNES, J. F. G.; COUTINHO, A. C.; ESQUERDO, J. C. D. M. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) a... ... Repository: BDPA Publication year: 2024
A review of deep learning algorithms for computer vision systems in livestock. Author(s): PEREIRA, L. G. R. In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to... ... Repository: BDPA Publication year: 2021
Sugarcane planting failure classification through deep learning approach in drone imagery. Author(s): YANO, I. H.; SPERANZA, E. A.; BARBOSA, L. A. F.; CANÇADO, G. M. de A. Este trabalho realizou testes com a rede YOLOv5, que é uma rede de aprendizado profundo, para a qual existem diversos tutoriais de utilização e aplicativos na internet que auxiliam na marcação dos obj... ... Repository: BDPA Publication year: 2022
Assessment of uav-based deep learning for corn crop analysis in midwest Brazil. Abstract: Crop segmentation, the process of identifying and delineating agricultural fields or specific crops within an image, plays a crucial role in precision agriculture, enabling farmers and publi... ... Repository: BDPA Publication year: 2024
SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms. Author(s): ANTUNES, J. F. G.; COUTINHO, A. C.; ESQUERDO, J. C. D. M. In this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping... ... Repository: BDPA Publication year: 2023
Deep learning regression approaches applied to estimate tillering in tropical forages using mobile phone images. Author(s): SANTOS, M. F.; JANK, L. We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 l... ... Repository: BDPA Publication year: 2022

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