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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
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Resultados da Pesquisa
Item Monitoring the vegetative state of coffee using vegetation indices(Associação Brasileira de Engenharia Agrícola, 2024) Chedid, Vitor; Cortez, Jorge W.; Arcoverde, Sálvio N. S.Vegetation indices are a quick and practical alternative for monitoring crops due to the availability of satellite images on various platforms for free, allowing a quick analysis of the vegetative state of the crop and interventions in the field in case of signs of diseases and pests. In this context, this study aimed to evaluate the vegetative state of the coffee crop using vegetation indices (NDVI, SAVI, ARVI, EVI, and VDVI) in an agricultural year. The study was carried out on a commercial farm using satellite images from the Planet platform, during an agricultural coffee growing season (2021/2022). The indices selected for the study were the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Atmospherically Resistant Vegetation Index (ARVI), Enhanced Vegetation Index (EVI), and Visible Difference Vegetation Index (VDVI). The index data were analyzed using descriptive statistics, Pearson correlation, classification/interpretation proposal, and the Kappa index. NDVI and SAVI are efficient in monitoring coffee cultivation in an agricultural year, as the Kappa index was higher than 90%. ARVI and EVI had Kappa index values close to 90% and can be used to monitor the crop. VDVI was inefficient, with a low Kappa index value when compared to the others. The proposed classification for vegetation indices based on NDVI classes and values consisted of an important tool for classifying and interpreting the values of these indices, assisting monitoring and management of coffee cultivation.Item Diagnosis about the perspectives of precision applications of coffee growing technologies in municipalities of Bahia, Brazil(Universidade Federal de Lavras, 2022-06-09) Fagundes, Rozymario Bittencourt; Bolfe, Édson LuisBrazil is the largest coffee producer in the world and precision agriculture (PA) is essential for the efficient management of crops. However, one of the challenges is finding the best way to do it. In this sense, we sought to present in this article a diagnosis on the perspectives of Precision Agriculture technologies applicability in the production of coffee (or Precision Coffee Growing) in some municipalities in Bahia, for greater efficiency, economic and environmental sustainability. To achieve this objective, a virtual document was sent to coffee growers in the state of Bahia. The questionnaire was sent by email in 2021 and the WhatsApp application, reaching 457 producers, 34 of whom, from all productive regions of Bahia, responded. The rate of return was 7.4%, within the expected by the use of the application. Considering the return of 34 answered questionnaires, a margin of error of 14% was obtained at a reliability level of 90%. It was found that 59.3% of the respondents have a high prospect of using PA in coffee growing, 26.6% have a medium perspective and 11.1%, a low perspective. The research shows that 67.6% do not use PA in the fields and that 51.7% consider the lack of training as a major obstacle to the use of PA and other digital technologies. Thus, the conclusion is reached that there is a promising scenario in Bahia state for the application of PA in coffee growing, if there is training for the development of techniques in farming.Item Comparing a single-sensor camera with a multisensor camera for monitoring coffee crop using unmanned aerial vehicles(Associação Brasileira de Engenharia Agrícola, 2021) Gomes, Amanda P. A.; Queiroz, Daniel M. de; Valente, Domingos S. M.; Pinto, Francisco de A. de C.; Rosas, Jorge T. F.There exist two options for digital cameras that can capture the near-infrared (NIR) band. Conventional red–green–blue (RGB, visible bands) cameras with a single sensor provide NIR band visibility based on the removal of the internal NIR-blocking filter. Alternatively, multisensor cameras exist that have a specific sensor for each band. The modified RGB cameras are of a lower price. In this context, the objective of this study was to compare the performance of a modified RGB camera with that of a multisensor camera for obtaining the normalized difference vegetation index (NDVI) in an area with coffee cultivations. A multispectral camera with five sensors and another camera with only one sensor were used. The NDVI of the coffee field was also measured using the GreenSeeker handheld NDVI sensor manufactured by Trimble. The images were calibrated radiometrically based on the targets in shades of gray made of napa, and the NDVI was calculated after image calibration. The calibration curves showed a high coefficient of determination. The NDVI value obtained with the calibrated images from the cameras showed a significant correlation with the values obtained by the GreenSeeker NDVI sensor, making it possible to obtain the variability pattern of the vegetation index. However, the NDVI obtained using the multisensor camera was closer to the NDVI obtained by the GreenSeeker NDVI sensor.Item Spatial correlation between the chlorophyll index and foliar npk levels in coffee crop(Editora UFLA, 2020) Zanella, Marco Antonio; Rodrigues Junior, Francelino Augusto; Sousa, Emanoel Di Tarso dos Santos; Martins, Rodrigo Nogueira; Calijuri, Maria LúciaDetection of spatial variability of data that can improve crop management is a key factor for precision agriculture. In agriculture, there is a need for tools to assist farmers in decision-making about proper nutrient management, aiming to achieve their full productive potential. Based on that, this study aimed to (1) determine the spatial correlations between the chlorophyll index (CI) and the foliar levels of nitrogen, phosphorus and potassium (NPK) in the coffee crop using geostatistical tools; and (2) to evaluate the potential use of this index as a tool for site-specific nutrient management in an irrigated coffee field. For that, a study was carried out in a 2.1 ha area under arabica coffee cultivation in Paula Cândido, Minas Gerais State, Brazil. Samplings of the CI were performed in 1141 plants using a portable chlorophyll meter (SPAD-502). Regarding the NPK analysis, leaf samples from one of each 10 plants used to measure the CI were taken for chemical analysis (114 plants). Then, the data were submitted to descriptive and geostatistical analysis. For the spatial correlation analysis, the Moran Bivariate Global (I) and the Local index (Ixy) were used. The results showed a moderate correlation between the CI and N (0.500), showing the potential of the chlorophyll meter as a tool for site-specific nitrogen management in the coffee crop. Differently, the CI is not recommended for P and K management since they were not well correlated. Lastly, as a tool that performs indirect measurements, the results from the chlorophyll meter should be validated by field measurements to local calibrations.Item Technical and economic viability of manual harvesting coffee yield maps(Editora UFLA, 2020) Faria, Rafael de Oliveira; Silva, Fábio Moreira; Ferraz, Gabriel Araújo e Silva; Herrera, Miguel Angel Diaz; Barbosa, Brenon Diennevan Souza; Alonso, Diego José Carvalho; Soares, Daniel VeigaPrecision coffee growing is a concept that implies the use of precision agriculture techniques in coffee plantations. For the coffee growing, the precision electronic resources coupled to the harvesters are very scarce. Thereby, the harvest of coffee plantations that compose the grid sampling for generation of thematic maps can be performed manually. The aim of the present study was to generate a linear regression model to estimate the time required to harvest, estimate the labor costs to harvest manually the georeferenced sample points for generation of coffee yield maps. The study was performed in a coffee area of 56 hectares using two sampling points per hectare, totaling 112 points, being evaluated four coffee plants for each point. The manual harvest of the points was performed by four rural workers with experience in the coffee harvest. Afterwards, the collected volume was measured by a graduated container and the times were obtained by the digital stopwatch. Based on the data obtained in the field, a linear correlation model was established between the harvest time of each sampling point and the yield of the point, whose R² value was 78.27, cost was R$ 8.92 per point. These results are relevant for estimating the amount of labor force required to generate manually harvest yield maps according to the producer’s coffee yield estimate, contributing to the closure of the precision coffee growing cycle.