Anderson Gomide Costa

Purpose: Unmanned aerial vehicle (UAV)-based remote sensing can support non-destructive and spatial pasture monitoring, particularly in heterogeneous tropical systems. This study assessed the potential of UAV-derived vegetation indices (VIs) from the visible and near-infrared (NIR) spectrum to estimate canopy height and leaf area index (LAI) of ‘Tifton 85’ bermudagrass under conditions characterized with high spatial variability. Methods: Mean canopy height and LAI were measured at 55 georeferenced sampling points during five field campaigns between May 15 and June 22, 2023. Multispectral UAV imagery was used to derive 10 VIs. Relationships between VIs and observed variables were assessed using Spearman’s correlation coefficient (R _s ). Simple nonlinear models were fitted and assessed using the coefficient of determination (R²) and the standard error of estimate (SEE), and tested with an independent dataset using R², root mean square error (RMSE), and the modified Willmott index (d _mw ). Results: Near-infrared (NIR)-based indices showed the strongest relationships with pasture parameters. Similar to LAI, height was strongly correlated with some VIs (R _s > 0.80) and good fit to the nonlinear models (R² > 0.64). However, height model performance testing was poor (R² < 0.25, d _mw < 0.49, and nRMSE > 40%) and lower than that obtained for LAI (R² < 0.72, d _mw < 0.64, and nRMSE > 22%). Overall, the best performances were obtained with polynomial models using Normalized Difference Vegetation Index (NDVI) or Visible Atmospherically Resistant Index (VARI) for height and exponential or power models using Green Normalized Difference Vegetation Index (GNDVI) for LAI. Conclusion: UAV-derived multispectral VIs showed potential for estimating and spatially modeling ‘Tifton 85’ parameters, especially LAI, under heterogeneous tropical field conditions.

Spatial and temporal variability of spectral responses creates challenges for developing models to estimate crop growth attributes, requiring more robust modeling approaches. This study proposes a protocol to generate multivariate models that estimate the growth attributes of Tifton85 forage (Cynodon spp.) from spectral responses derived from multispectral images by unmanned aerial vehicles. The experiment was conducted in a production area in Seropédica, Rio de Janeiro, Brazil. At each evaluation date, plant chlorophyll index, plant height, and leaf area index were measured. Multispectral images captured by a camera mounted on an unmanned aerial vehicle were also collected to calculate vegetation indices. Principal component analysis was used to develop an index that integrates vegetation indices over the crop growth period (CGI, crop growth index). Estimation models and spatial variability maps were generated from the linear relationship between crop growth attributes and the CGI. The responses of the vegetation indices were combined into a single principal component, which explained 99.94% of the variance. Models based on the CGI achieved greater accuracy than univariate models and produced spatial-variability maps consistent with observed crop growth attributes. These findings indicate that the CGI has strong potential for estimating Tifton85 growth attributes and is particularly effective for crops exhibiting high spatial and temporal variability.

Computer vision systems have been successfully applied to automate processes related to the selection and classification of food based on quality standards. In this context, this research aimed to develop a computer vision system comprising an image acquisition device and software capable of estimating the physical parameters of tomato fruits from digital images. The software was developed in Python and implemented on a Raspberry Pi microprocessor, enabling both image capture and the calculation of fruit physical parameters (volume, sphericity, and surface area). The physical parameters of 45 tomatoes from three classes (Saladette, Italian, and Cherry) were estimated using the proposed system and compared with reference values obtained through traditional methods. The comparison indices revealed a high correlation and agreement between actual and estimated values for all physical parameters when analyzing fruits across all classes. The models generated from the total set of fruits (without class distinction) achieved R2 values greater than 0.95 for all physical parameter estimates. The computer vision system enabled the measurement of tomato physical parameters, presenting approximate estimates to the reference values obtained by traditional methods. This approach represents a viable alternative for estimating the sphericity, surface area, and volume of tomato fruits.

Aim of study: The present work addresses aspects related to the development of a prototype of a weeder capable of managing invasive plants in vegetable beds. Area of study: The investigation was performed in the Federal Rural University of Rio de Janeiro, Seropédica (Brazil). Material and methods: The methodology was divided into defined steps from planning and conceptual design, obtaining preliminary and detailed designs, to evaluating the performance of the physical prototype. The variant considered most appropriate was selected according to the project’s feasibility index and was chosen for the development of the preliminary design and detailed design. Main results: Based on the results obtained, it can be stated that the concepts used, and the prototype developed were functional and innovative for weed control in a lettuce crop, enabling the mechanical removal of weeds from 70.3% of the plants and increasing the working area by 49.3%. This indicates improved efficiency in the vegetable production process in raised beds. Research highlights: The developed and validated mechanical weeder prototype allowed efficient weed removal and greater operational efficiency during lettuce cultivation in raised beds.

Omputer vision systems have proven to be a promising alternative for assessing fruit quality attributes in a non-invasive, instantaneous, and accurate manner. This study aimed to use colorimetric characteristics extracted from digital images to estimate tomato fruit firmness through multivariate modeling. Images of 80 tomato fruits at four ripening stages were acquired using two digital cameras, enabling the extraction of average intensity values for the red, green, blue, and near-infrared bands, followed by the calculation of colorimetric indices. Reference firmness values were measured using a digital fruit penetrometer. Colorimetric indices were employed to estimate fruit firmness using principal component regression. Principal component analysis enabled the dimensionality to be reduced to a single principal component (explanatory percentage of the data variance of 97.06%), which was used to generate firmness estimation equations. The application of the model to the validation dataset yielded an R2 = 0.937 and a mean standard error (SE) of 2.05 N, demonstrating that the protocol based on colorimetric characteristics extracted from digital images is suitable for estimating tomato firmness.

Computer vision systems combined with machine learning techniques have demonstrated success as alternatives to empirical methods for classification and selection. This study aimed to classify tomatoes based on their colorimetric characteristics, which influence consumer purchasing potential, using the decision tree algorithm. Tomatoes were categorized into two classes based on ripeness: Higher Purchasing Potential (20 fruits) and Lower Purchasing Potential (40 fruits). Images were captured in the RGB color model and converted to HSI and CIELab models. Principal component analysis was employed to evaluate the influence of colorimetric characteristics within each class, and the decision tree algorithm was applied to classify the fruits into the respective categories. Tomatoes in the Higher Purchasing Potential class were primarily influenced by red intensity and chromaticity a and b, while tomatoes in the Lower Purchasing Potential class were influenced by green intensity and hue. The decision tree achieved an accuracy of 83.6% and an F1-score of 90.9%, demonstrating its potential for classifying tomatoes based on colorimetric characteristics linked to consumer preferences.

Water erosion is one of the primary causes of agricultural soil degradation in Brazil, leading to diminished crop productivity and soil acidification, thereby impairing its ability to store carbon, nutrients, and water. Identifying the intensity of erosion can be achieved by utilizing indicator attributes that are highly sensitive to changes in the edaphic environment. The study analyzed the physical and chemical attributes of soil in areas with gullies exhibiting varying degrees of degradation/stabilization. The study was conducted across four areas with varying degrees of gully formation: a) initial, intermediate, mature, and senile. Samples were collected from both the external and internal sides of each gully at the end of the dry season. Among the physical attributes assessed, soil density and total porosity were found to be the most significantly altered. Evaluation of microaggregates provided insights into soil quality through fractions including water-dispersible clay, water-reflocculable clay, and non-water-dispersible clay. Total carbon, total nitrogen, and the C/N ratio elucidated the dynamics of soil and nutrient loss across different stages of erosion formation and stabilization processes, with lower values observed on the internal side of the gullies compared to the exterior side. Vegetation was observed to influence the results of the physical and chemical attributes. Overall, the values tended towards equilibrium between the faces at the senile stage, indicating greater stabilization. Keywords: agricultural sustainability, agroecosystem degradation, food security, soil attributes.

Métodos de engenharia devem ser constantemente atualizados para promover avanços construtivos, econômicos e sustentáveis. Neste estudo, novos dados sobre valorização energética e tratamento de águas residuárias de bovinos por meio de um reator UASB operado a 40°C foram obtidos. Foi aplicado um regime de alimentação semi-contínuo, com tempos de retenção hidráulica de 6, 5, 3 e 2 dias, e taxas de carga orgânica de 4, 5, 7 e 11 kg COD m-3 d-1. Foram registrados volumes de gás de até 1,5 m³ de biogás m-3 de reator d-1, com concentrações de metano variando entre 69% e 75%. Remoções eficientes de matéria orgânica de 60-80% (DQO total) e 50-75% (DQO solúvel) resultaram em rendimentos de metano de 0,16-0,18 m³ CH4 kg-1 de DQO total e 0,4-0,5 m³ CH4 kg-1 de DQO solúvel. Não ocorreu acúmulo de ácidos graxos voláteis em nenhuma etapa, e os valores de pH e alcalinidade permaneceram dentro da faixa ideal para o desenvolvimento do processo de digestão anaeróbica. O reator UASB demonstrou produção significativa de biogás e eficiência razoável na remoção de sólidos e matéria orgânica, indicando-o como uma nova opção para o tratamento de águas residuárias com alta concentração de poluentes com o benefício da produção de energia limpa.