DSpace Collection:https://repositorio.ufu.br/handle/123456789/304982026-04-21T04:31:28Z2026-04-21T04:31:28ZBioestimulante sustentável de folhas de tomateiro anão: avaliação metabolômica e efeito em mudas de alface por imagens RGBhttps://repositorio.ufu.br/handle/123456789/482972026-02-14T06:21:17Z2025-12-17T00:00:00ZTitle: Bioestimulante sustentável de folhas de tomateiro anão: avaliação metabolômica e efeito em mudas de alface por imagens RGB
Abstract: Plant production practices that reduce the excessive use of chemical inputs are essential for sustainable agriculture. Dwarf tomato leaf extract, characterized by its richness in secondary metabolites, represents a promising sustainable bio-stimulant for vegetable crops. This study aimed to evaluate the potential of leaf extract from the dwarf tomato genotype UFU MC TOM1 as a sustainable biostimulant in lettuce seedlings using RGB image analysis, as well as to identify the metabolites present in the leaves by gas chromatography–mass spectrometry. The experiment was conducted under controlled conditions in a spray test chamber, using lettuce
seedlings grown in trays and subjected to three spray applications of leaf extracts from two tomato genotypes (UFU MC TOM1 and Santa Clara) at different concentrations (50%, 75%, and 100%), with distilled water as the control. Evaluations included fresh and dry mass, shoot length, and root length. Application of UFU MC TOM1 leaf extract promoted significant increases in fresh and dry mass, especially at concentrations of 50%, 75%, and 100%. Shoot length was also favored, particularly in the 75% and 100% treatments, while root length and total seedling length did not show consistent gains. Multivariate analysis confirmed the superior performance of UFU MC TOM1 extracts, and RGB image analysis corroborated the manual measurements. Metabolomic characterization revealed higher abundance of amino acids, carbohydrates, and carboxylic acids in UFU MC TOM1 compared to Santa Clara, with
emphasis on L-serine, L-erythrulose, adenine, and urea, reinforcing its potential as a natural and sustainable biostimulant for agricultural systems.2025-12-17T00:00:00ZModelagem do crescimento e produção de florestas plantadas usando indicadores de capacidade fotossintética derivados de sensoriamento remoto e aprendizado de máquinashttps://repositorio.ufu.br/handle/123456789/482362026-02-23T20:43:37Z2025-12-12T00:00:00ZTitle: Modelagem do crescimento e produção de florestas plantadas usando indicadores de capacidade fotossintética derivados de sensoriamento remoto e aprendizado de máquinas
Abstract: Forest growth and yield modeling is fundamental for sustainable planning and management of planted forests. The Clutter model, widely used in Brazil, relies exclusively on conventional dendrometric variables (age, basal area, and site index), without considering physiological factors such as photosynthetic capacity. This study aimed to evaluate the incorporation of proxy variables for photosynthetic capacity, derived from remote sensing, into forest growth models, comparing the performance of the traditional Clutter model, its modified variant with spectral and textural indices, and machine learning algorithms. The research was conducted in commercial eucalyptus plantations in the Vale do Rio Doce region, Minas Gerais, using continuous forest inventory data (2013-2019) provided by CENIBRA. Reflectance values were extracted from cloud-free Landsat 8 images, calculating eight spectral indices (NDVI, GNDVI, SAVI, SR, NBRI, RVI, DVI, EVI) and eight texture indices based on the Gray Level Co-occurrence Matrix (Energy, Entropy, Correlation, IDM, Inertia, CS, CP, HC). Five machine learning algorithms were tested: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), and K-Nearest Neighbors (KNN). Validation was performed through 5-fold cross-validation, using RMSE, RMSE%, Pearson correlation (r), R², and BIAS% metrics. Results demonstrated that the traditional Clutter model exhibited high accuracy, with RMSE% below 10% for volume, R² greater than 0.93, and r = 0.97. The incorporation of spectral and textural indices into the Clutter model promoted modest improvements, reducing RMSE% from 8.51% to 8.46% with SR and NBRI indices. However, machine learning algorithms, particularly SVM, demonstrated superior capability in incorporating these variables, resulting in accuracy gains of up to 0.7% compared to the traditional model. The SR, NBRI, and HC indices stood out as the best predictors. It is concluded that the inclusion of remote sensing variables can discretely enhance the predictive capacity of traditional models, with machine learning algorithms being more efficient in incorporating these variables, indicating a promising path for improving forest growth and yield models.2025-12-12T00:00:00ZDistribuição espacial de nematoides entomopatogênicos e sua relação com variáveis edáficas em cafezais sob manejo regenerativohttps://repositorio.ufu.br/handle/123456789/481152026-01-30T06:32:58Z2025-12-19T00:00:00ZTitle: Distribuição espacial de nematoides entomopatogênicos e sua relação com variáveis edáficas em cafezais sob manejo regenerativo
Abstract: Entomopathogenic nematodes (EPNs) are promising allies in the integrated management
of coffee pests, but their dynamics in regenerative systems of the Cerrado Mineiro remain
poorly understood. We assessed the occurrence of EPNs in the soil and their relationship
with edaphic attributes in three commercial plots of Coffea arabica L. under regenerative
management. EPNs were isolated using Tenebrio molitor L. (Coleoptera: Tenebrionidae)
larvae as bait and the White trap method, and occurrence was modeled using logistic
regression at two scales. Soil compaction did not explain the presence of EPNs within plots;
between plots, soil moisture showed a consistent positive association, whereas β-glucosidase
and compaction were not determinant factors. The models showed good calibration and
moderate discrimination.We conclude that differences among plots and higher soil moisture
are associated with an increased probability of EPN occurrence, suggesting that monitoring
and water management should consider wetter zones as priority areas to enhance biological
control.2025-12-19T00:00:00ZAvaliação da Acurácia da Nuvem de Pontos Gerada por Processamento Fotogramétrico de Imagens Adquiridas por Sensores Embarcados em ARP para Levantamentos Topográficos de Alta Precisãohttps://repositorio.ufu.br/handle/123456789/479532025-12-31T06:21:28Z2025-11-28T00:00:00ZTitle: Avaliação da Acurácia da Nuvem de Pontos Gerada por Processamento Fotogramétrico de Imagens Adquiridas por Sensores Embarcados em ARP para Levantamentos Topográficos de Alta Precisão
Abstract: The advancement of geotechnologies and the consolidation of Remotely Piloted Aircraft (RPA) have significantly expanded the use of digital photogrammetry in the generation of high-resolution cartographic products, especially for topographic surveys and agricultural applications. In this context, this dissertation aimed to evaluate the three-dimensional accuracy of the point cloud generated through photogrammetric processing of images acquired by sensors onboard an RPA, using observations obtained with a Global Navigation Satellite System (GNSS) receiver operating in Real Time Kinematic (RTK) mode as reference. The study area corresponds to a section of Highway MG-223, between kilometers 36 and 39, in the municipality of Estrela do Sul, Minas Gerais, Brazil, characterized by rugged terrain and significant altimetric variation, totaling approximately 60 hectares. A total of 44 ground control points were established, including 26 Ground Control Points (GCPs) and 18 Check Points (CKPs), whose coordinates were determined using a dual-frequency GNSS receiver and referenced to the RBMC MGMT station in Monte Carmelo, Minas Gerais. The aerial photogrammetric survey was conducted following an appropriate flight plan, and the images were processed using the Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms in Agisoft Metashape, resulting in a dense and georeferenced point cloud. External validation of the model was performed by comparing 2,350 GNSS-RTK points with the corresponding coordinates extracted from the point cloud, analyzing discrepancies in the East E(X), North N(Y), and Height h(Z) components, as well as statistical metrics such as RMSE, MAE, and maximum and minimum errors. The results indicated RMSE values below 5 cm for the horizontal components and below 10 cm for the vertical component, classifying the generated product as Class A according to the Brazilian Positional Accuracy Standard (PEC/EP) and Level 1 (highest accuracy) according to ASPRS (2014). It is concluded that RPA-based photogrammetry, when combined with rigorous geodetic control and appropriate methodological procedures, is capable of producing point clouds with high metric reliability, proving to be a technically viable and efficient alternative for high-precision topographic surveys and applications in agriculture and roadway infrastructure.2025-11-28T00:00:00Z