https://repositorio.ufu.br/handle/123456789/30498 2026-08-08T22:07:16Z 2026-08-08T22:07:16Z https://repositorio.ufu.br/handle/123456789/48962 2026-07-23T06:22:19Z 2026-02-23T00:00:00Z

Title: Discrimination of coffee diseases and pests using multispectral imaging Abstract: A identificação precisa de doenças e pragas em cafeeiros é crucial para o manejo dirigido da lavoura e para evitar perdas de produtividade. Neste estudo, utilizaram-se imagens multiespectrais para monitorar um cafezal irrigado por gotejamento da cultivar Topázio MG 1190, implantado em 2016 na Universidade Federal de Uberlândia – Campus Monte Carmelo, com o objetivo de discriminar sintomas causados por Leucoptera coffeella (bicho mineiro), Cercospora coffeicola (cercosporiose) e Hemileia vastatrix (ferrugem do cafeeiro). As imagens, capturadas mensalmente de fevereiro a novembro de 2024 com um drone DJI Phantom 4, foram analisadas por meio de modelos de classificação supervisionada de aprendizado de máquina (Redes Neurais e Random Forests) desenvolvidos no software Weka. Paralelamente, foram avaliados 4 pares de folhas por planta (terço médio) para analisar a incidência de cada doença/praga. Os dados de entrada para as classificações consistiram nas bandas espectrais originais do sensor, nos índices de vegetação derivados e na classe designada por planta. Posteriormente, a metodologia foi testada em uma lavoura comercial em julho de 2025. Os resultados demonstraram que os modelos de classificação atingiram até 85% de acurácia e índice Kappa de 0,8, evidenciando o potencial do uso de imagens multiespectrais para o monitoramento contínuo das principais doenças e pragas da cafeicultura. Além disso, os mapas gerados pelos modelos permitiram analisar a distribuição espacial dos problemas fitossanitários estudados, mostrando-se uma ferramenta de apoio a práticas de manejo localizado. Desse modo, a pesquisa evidenciou que essa ferramenta pode ser uma alternativa para a promoção da cafeicultura sustentável, contribuindo para o controle mais eficiente de doenças e pragas.

2026-02-23T00:00:00Z https://repositorio.ufu.br/handle/123456789/48923 2026-07-21T06:27:39Z 2026-01-20T00:00:00Z

Title: Guiafertil: software de análise exploratória e predição do limiar de produtividade da cultura de soja com técnicas de inteligência artificial Abstract: Soybean cultivation plays a fundamental role in Brazilian agribusiness, requiring increasingly efficient management practices to ensure high levels of productivity and sustainability. However, the criteria currently used by agronomists to recommend fertilizer and soil amendment rates exhibit considerable variability, mainly due to the lack of regional calibration studies that simultaneously consider specific soil attributes, climatic conditions, altitude, planting systems, and other variables that influence crop development. This limitation hinders technical decision-making in the field and reduces the accuracy of agronomic recommendations. In this context, understanding the relationship between these factors and crop productivity becomes essential to enable more robust estimates and support management decisions. Advances in Artificial Intelligence (AI) techniques offer new opportunities to model such relationships and identify complex patterns that are often difficult to detect using traditional approaches. Therefore, this study presents the development of Guiafertil, a computational tool designed for exploratory analysis of agronomic data and soybean yield threshold prediction. Guiafertil integrates a database management system, data preprocessing routines, statistical analysis, graphical visualization, and machine learning algorithms, including Linear Regression, Support Vector Regression (SVR), Random Forest, and Artificial Neural Networks. Using real-world data obtained from the “Champion Cases” database of the Brazilian Soybean Strategic Committee (CESB), the system enables the evaluation of soil, climate, and management attributes, model training, performance analysis, and productivity estimation based on practical scenarios, such as different fertilization rates. Among the main results, the Multi-Layer Perceptron (MLP) Neural Network achieved the best predictive performance, reaching an R² value of 0.56 for soybean yield prediction bags/ha, whereas Linear Regression showed significant predictive limitations, obtaining an R² value of -0.60. Furthermore, the case study demonstrated that adjustments in fertilizer application rates affect production costs but have limited impact on predicted productivity, highlighting the importance of more accurate decision-making processes. The software quality and usability were evaluated by agronomists, who assigned average scores above 9.0 for most of the assessed criteria. Therefore, Guiafertil proved to be a promising tool for supporting agronomic recommendations, contributing to more efficient use of agricultural inputs, cost reduction, and greater sustainability in soybean production. Additionally, this work opens new opportunities for future research involving other crops, expansion of the database, and integration with precision agriculture technologies.

2026-01-20T00:00:00Z https://repositorio.ufu.br/handle/123456789/48770 2026-07-10T18:57:15Z 2026-03-06T00:00:00Z

Title: Diversidade espacial e prospecção de fungos fitopatogênicos para o controle biológico do cipó preto (Adenocalymma impressum) Abstract: The black liana (Adenocalymma impressum) poses a significant challenge to comercial Eucalyptus plantations in the eastern Amazon owing to its rapid growth and resistance to conventional herbicide-based control. This study aimed to prospect and characterize phytopathogenic fungi associated with A. impressum for potential use in biological control. Fungal isolates were collected from symptomatic plants in Maranhão, Brazil, resulting in 124 isolates, of which 24 were pathogenic. Among these, Lasiodiplodia theobromae SC-35 exhibited rapid symptom development, extensive tissue colonization, and host specificity, while remaining nonpathogenic to eucalyptus clones. Environmental factors, including temperature and native forest remnants, influenced fungal diversity and aggressiveness. These findings demonstrate the potential of SC-35 as candidate for development of a sustainable and effective bioherbicide for managing black liana, highlighting the value of endemic phytopathogens in integrated forest weed management strategies.

2026-03-06T00:00:00Z https://repositorio.ufu.br/handle/123456789/48297 2026-02-14T06:21:17Z 2025-12-17T00:00:00Z

Title: 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.

2025-12-17T00:00:00Z