Biblioteca do Café
URI permanente desta seção${dspace.url}/handle/123456789/1
Navegar
4 resultados
Resultados da Pesquisa
Item Quality of coffee planting techniques by aerial sensors and statistical process control(Universidade Federal do Ceará, 2024) Santana, Lucas Santos; Ferraz, Gabriel Araújo e Silva; Cunha, João Paulo Barreto; Marin, Diego Bedin; Bento, Nicole Lopes; Faria, Rafael de OliveiraPlanting is considered one of the most essential steps in coffee growing. Lack of uniformity in planting may compromise future operations. Therefore, verifying planting operations quality is fundamental to optimizing production processes and reducing costs. This study aimed to investigate planting techniques through Statistical Process Control (SPC) and aerial images. Carried out in two areas, managed manually and semi-mechanized in the Bom Jardim Farm (MG - Brazil). Data were collected through Remotely Piloted Aircraft (RPA). Quality control charts and density maps were used to identify variations in distribution and spacing between plants and planting rows. It was found that the planting carried out manually was 4.7% wider than projected due to spacing reduction from 0.5 m to 0.48 m. The semi-mechanized system displayed a deficit of 7% compared to the projected planting system, using 0.55 m between plants. The density map showed the most significant planting alignment variations. Despite displaying lower results than the manual system, the semi-mechanized system improvements are valid for their minimal average variations. Thus, correcting points found outside the limits can increase the efficiency of semi-mechanized planting.Item Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System(Academia Brasileira de Ciências, 2023-11-03) Bento, Nicole L.; Ferraz, Gabriel Araújo E.S.; Barata, Rafael Alexandre P.; Soares, Daniel V.; Teodoro, Sabrina A.; Estima, Pedro Henrique De O.The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and export of coffee in Brazil and the technological application of the Remotely Piloted Aircraft System (RPAS) this study aimed to compare and select models based on different data classification techniques by different classification algorithms for the prediction of different coffee cultivars (Coffea arabica L.) recently planted. The attributes evaluated were height, crown diameter, total chlorophyll content, chlorophyll A and chlorophyll B, Foliar Area Index (LAI) and vegetation indexes NDVI, NDRE, MCARI1, GVI, and CI in six months. The data were prepared programming language Python using algorithms of Decision Trees, Random Forest, Support Vector Machine and Neural Networks. It was evaluated through cross-validation in all methods, the distribution by FreeViz, the hit rate, sensitivity, specificity, F1 score, and area under the ROC curve and percentage and predictive performance difference. All algorithms showed good hits and predictions for coffee cultivars (0.768% Decision Tree, 0.836% Random Forest, 0.886 Support Vector Machine and 0.899 Neural Networks) and the Neural Networks algorithm produced more accurate predictions than other tested algorithm models, with a higher percentage of hits for the classes considered.Item Remotely piloted aircraft and computer vision applied to coffee growing management(Universidade Federal de Lavras, 2022-11-25) Santana, Lucas Santos; Ferraz, Gabriel Araújo e SilvaDigital and precision agriculture technologies used in coffee farming have gained space and have become necessary in many coffee production stages. Among the emerging technologies, the Remotely Piloted Aircraft (RPA) can be highlighted because their products can be used as data providers for machine learning techniques and automated monitoring forms. This study aimed to apply cartographic and photogrammetric products from RPAs submitted to machine learning techniques and image analysis in digital and precision coffee farming. Three types of research were built: Application of RPA cartographic products for the coffee plant implantation project; Identification and counting of plants in PRA images and Investigations of plants development in renewal areas. (I)The first study evaluated different flight mission composition efficiency and point cloud levels for Digital Terrain Models generation applied in coffee plantations. Flights performed at 120 m Above Ground Land (AGL) and 80 × 80% overlap showed higher assertiveness and efficiency. The 90 m AGL flight showed great terrain detail, causing significant surface differences concerning the topography obtained by Global Navigation Satellite System (GNSS) receivers. Slope ranges up to 20% are considered reliable for precision coffee growing projects. Changes in flight settings and image processing are satisfactory for precision coffee projects. Image overlap reduction significantly lowed the processing time without influencing Digital Terrain Model DTM's quality. (II) The second research aimed to develop an algorithm for automatic counting coffee plants and define the plant's best age to carry the monitoring using RPA images. Plants with four months of development showed 86.5% count assertiveness. The best results were observed in plantations with six months of development, presenting an average of 96.8% of assertiveness in automatically counting plants. This analysis enables an algorithm development for automated counting of coffee plants through RGB images obtained by remotely piloted aircraft and machine learning applications. (III) The objective of the third research was to monitor the coffee plants' development planted on ash from crop residues through vegetative indices in RPA images, analysis of chemical elements presents in the ash and soil analysis. Preliminary results indicate the high presence of aluminum and potassium in the ash, causing significant differences in coffee development beginning. In addition, variations were observed in vegetative indices values in regions with ash presence, highlighting the NGI and NNIRI indices. The research developed by this paper provides essential information for digital agriculture technologies advancement in coffee growing.Item Relationship between coffee crop productivity and vegetation indexes derived from oli / landsat-8 sensor data with and without topographic correction(Associação Brasileira de Engenharia Agrícola, 2018-05) Nogueira, Sulimar M. C.; Moreira, Maurício A.; Volpato, Margarete M. L.The reflectance values of a coffee crop are influenced by several factors such as planting direction, crop spacing, time of the year, plant age and topography which reduces the accuracy of the estimates derived from remote sensing data. In this context were evaluated the relationships between coffee productivity and values of NDVI, SAVI and NDWI vegetation indexes with and without topographic reflectance correction for different coffee phenological phases for the crop years 2013/2014 (low productivity) and 2014/2015 (high productivity). The evaluations were made through the standard deviation of vegetation indices (VIs), linear relationship between the cosine factor and the VIs and between VIs and coffee productivity. The best phenological phases of coffee to determine productivity from spectral indexes were the stages of dormancy and flowering. The results indicated that the NDVI was the best index to estimate the productivity of coffee trees with coefficient of determination (R2) that ranged from 0.58 to 0.90. There was an increase in R2 between productivity and NDVI with topographic correction in the dormancy phase in the year of low productivity; between productivity and NDVI with topographic correction in the flowering phase in the year of high productivity; and between productivity and SAVI and NDWI with topographic corrections in the flowering phase in the year of high productivity.