Gaussian Process Regression as an Alternative to Kriging and SVM for Spatial Yield Prediction

Detecting spatial yield variability is essential for precision agriculture, as it reduces environmental impact and improves economic returns. This study evaluates Gaussian Process Regression (GPR), Ordinary Kriging (OK), and Support Vector Machine (SVM) under different sampling densities. GPR and OK perform similarly, with GPR showing a slight advantage in low-sampling conditions. With 322 samples, GPR achieves higher accuracy (RMSE = 0.64 t/ha, R² = 0.68) than OK (RMSE = 0.72 t/ha, R² = 0.60), while SVM performs worse (RMSE = 0.76 t/ha, R² = 0.55). Regardless of sample size, SVM-generated maps exhibit a smoothing effect, reducing sensitivity to local variations. OK remains effective but is more sensitive to sample density due to its reliance on the semivariogram model and the assumption of isotropy. These findings highlight GPR as a robust method for spatial yield prediction, particularly in sparse data conditions. The study was conducted in Patos de Minas, Brazil, using 795 georeferenced soybean yield samples over 3.7 hectares. From a practical perspective, GPR and OK remain strong candidates for yield interpolation, reinforcing the importance of model selection based on data availability and spatial variability.Detecting spatial yield variability is essential for precision agriculture, as it reduces environmental impact and improves economic returns. This study evaluates Gaussian Process Regression (GPR), Ordinary Kriging (OK), and Support Vector Machine (SVM) under different sampling densities. GPR and OK perform similarly, with GPR showing a slight advantage in low-sampling conditions. With 322 samples, GPR achieves higher accuracy (RMSE = 0.64 t/ha, R² = 0.68) than OK (RMSE = 0.72 t/ha, R² = 0.60), while SVM performs worse (RMSE = 0.76 t/ha, R² = 0.55). Regardless of sample size, SVM-generated maps exhibit a smoothing effect, reducing sensitivity to local variations. OK remains effective but is more sensitive to sample density due to its reliance on the semivariogram model and the assumption of isotropy. These findings highlight GPR as a robust method for spatial yield prediction, particularly in sparse data conditions. The study was conducted in Patos de Minas, Brazil, using 795 georeferenced soybean yield samples over 3.7 hectares. From a practical perspective, GPR and OK remain strong candidates for yield interpolation, reinforcing the importance of model selection based on data availability and spatial variability

Evaluation of Three-Dimensional Photogrammetric Model from Low-Cost Digital Camera Applied in the Cultural Heritage Registry

The technologies such as the Laser Scanner stand out within the area of architectural registry. Laser Scanner represents a precise technology, which enables the creation of three-dimensional models through the acquisition of milions of points. On the other hand, photogrammetric techniques are also used for similar purposes, making use of images in order to generate three-dimensional representation. Both methodologies aim to obtain reliable representations of reality. However, there are potential discrepancies regarding the accuracy and costs of generating these models. The objective of this letter is to compare the models generated by terrestrial Photogrammetry and Laser Scanner, in order to evaluate the feasibility and efficiency of conventional cameras applied to three-dimensional models for facade restoration purposes. For that, the surveys by terrestrial Photogrammetry and Laser Scanner were used on the facades of a century-old church in Monte Carmelo, MG. From the products, it was observed that the distance between the surfaces generated by the clouds of points from the photogrammetric process and Laser was less than 5 cm

CARACTERIZAÇÃO ESPECTRAL DA CANA-DE-AÇÚCAR INFECTADA POR NEMATOIDES E MIGDOLUS FRYANUS POR ESPECTRORRADIOMETRIA DE CAMPO

O cultivo da cana-de-açúcar no Brasil, embora assistido por técnicas modernas de plantio, é alvo constante de parasitas do sistema radicular. Por registrar seletivamente o fluxo espectral da radiação eletromagnética refletida pela vegetação, o sensoriamento remoto tornou-se uma poderosa ferramenta na detecção das plantas infectadas por patógenos do solo. Com o objetivo de caracterizar espectralmente a cana-de-açúcar sadia e infectada por nematoides e pela larva do besouro Migdolus fryanus, foram tomadas medidas radiométricas in situ e geradas curvas hiperespectrais de plantas sadias e infectadas. Técnicas específicas de análise espectral, como a determinação da posição da borda do vermelho limítrofe (Red Edge Position Determination – REPD) e diferentes índices espectrais foram avaliados para discriminar as três ocorrências. As curvas de reflectância mostraram diferenças em magnitude principalmente nos comprimentos de onda do vermelho e infravermelho próximo e, assim como a determinação do REP e os índices de clorofila b, NDVI, MCARI e TCARI, permitiram distinguir apenas entre plantas sadias e infectadas. As razões espectrais sensíveis aos pigmentos clorofila a e carotenoides, porém, discriminaram as três ocorrências, inclusive plantas infectadas por nematoides e Migdolus fryanus. A melhor discriminação foi obtida com o índice de carotenoides, um pigmento fortemente relacionado com estresse da planta

RFJ Spatial variability in fertigated coffee yields and plant nutrients in soil saturation extracts

The spatial distribution and levels of available plant nutrients (elements) in the soil can limit coffee yield and must be evaluated for effective crop management. Therefore, we analyzed spatial variability in yield and plant nutrients in the saturation extract of a clayey Oxisol cropped with fertigated coffee. The experiment was carried out on 14 hectares of coffee in Monte Carmelo, Minas Gerais, Brazil.  Soil samples were collected (0 - 0.2m layer) at 61 regular grid points (spaced 50x50m) and used to determine plant nutrients in the saturation extract. Coffee yield was also determined at these points. Descriptive statistics were calculated for each variable and geostatistics were used to build a spatial variability model representing the physical attributes of the soil. Variographic analysis was performed using semivariograms. These showed that yield and soil chemistry varied throughout the study site. Thus, the maps generated from geostatistics can be useful tools for soil management in fertigated coffee crops

Spatial variability in the physical properties of an Oxisol under coffee cultivation in the Brazilian Cerrado

The physical properties of the soil are limiting factors for coffee cultivation and yields.  Therefore, we analyzed spatial variability in the physical properties of a clayey Oxisol under coffee cultivation. The experiment was carried out on 14-hectares of a coffee (Coffea arabica) plantation in the city of Monte Carmelo, in the Brazilian state of Minas Gerais. Soil samples were collected from two layers (0 - 0.1 m and 0.1 - 0.2 m) at 61 grid-points spaced at 50 x 50 meters.  These samples were saturated to determine total porosity and soil bulk density. Soil resistance readings were also taken from the same grid points and layers using an impact penetrometer. Descriptive statistics were used to evaluate all variables. Additionally, geostatistics were used to model spatial variability within the soil physical properties. Variographic analysis was performed using semivariograms. We found that density, total porosity and soil resistance to penetration varied throughout the study area, which demonstrates that management type can alter soil physical properties and that maps generated by geostatistics can help coffee growers make decisions related to soil management

Mapping Nutrients Content in a Nematode-Infected Coffee Plantation by Empirical Models Derived from RapidEye Image

Nematodes are among the most important coffee pathogens, causing significant losses of productivity. The infection of the coffee plant by nematodes can compromise the root system inducing the manifestation of reflex symptoms in its upper part. In addition, nutritional deficiencies may trigger an increase in host predisposition to various other pathogens. Thus, the monitoring of the nutritional levels of plants grown in areas predisposed to the occurrence of nematodes is fundamental. In this study, it was evaluated the potential of empirical models to estimate macro and micronutrient contents in an coffe experimental nematode infested area from a RapidEye multispectral image. For this purpose, laboratory analyzes were performed to determine the contents of macro and micronutrients, as well as the level of nematode infestation, in two experimental plots located in the coffee region of Monte Carmelo (MG). It was verified that the correlation between nutrient content and nematode concentration was higher for the Mg, S, Cu and Mn (correlation coefficients of 0.62, 0.51, 0.71 and 0.75, respectively), while other nutrients had higher correlations with spectral bands or vegetation indices, mainly Ca which had coefficients higher than 0.7 with all indices derived from the spectral bands of red, red edge and near infrared. Empirical models for nutrient estimation were generated from spectral bands and vegetation indices with correlations greater than 0.5. The red edge band, positioned in a spectral region sensitive to variations in vegetation, individually participated in the models to infer the concentrations of the macronutrients Mg and S, besides the micronutrients B, Cu, Fe and Mn, but all calibrated with correlation coefficients below 0,41. The near infrared band was used in the estimation of the N, P and Na contents (R2 equal to 0.25, 0.36 and 0.49, respectively). The NDVI participated in the formulation of the inference model of Ca content and resulted in the highest calibration R2 (0.61), although the validation error was high (13.56%). The choropleth maps of Ca, Mg, Cu, Fe, Mn and Zn spatial distribution had a similar configuration, indicating almost homogeneous and high concentrations of these nutrients in most of the experimental area. The Na and B contents were different in the two plots of the experimental area, while K and S had a more heterogeneous distribution. The maps of N and P reflect well the deficiency of these nutrients in the whole area, mainly in the P content. The empirical models adjusted for the estimation of most of the nutrients were consistent with the condition of excess or deficiency of nutrients in the experimental area

Mapping Nutrients Content in a Nematode-Infected Coffee Plantation by Empirical Models Derived from RapidEye Image

Nematodes are among the most important coffee pathogens, causing significant losses of productivity. The infection of the coffee plant by nematodes can compromise the root system inducing the manifestation of reflex symptoms in its upper part. In addition, nutritional deficiencies may trigger an increase in host predisposition to various other pathogens. Thus, the monitoring of the nutritional levels of plants grown in areas predisposed to the occurrence of nematodes is fundamental. In this study, it was evaluated the potential of empirical models to estimate macro and micronutrient contents in an coffe experimental nematode infested area from a RapidEye multispectral image. For this purpose, laboratory analyzes were performed to determine the contents of macro and micronutrients, as well as the level of nematode infestation, in two experimental plots located in the coffee region of Monte Carmelo (MG). It was verified that the correlation between nutrient content and nematode concentration was higher for the Mg, S, Cu and Mn (correlation coefficients of 0.62, 0.51, 0.71 and 0.75, respectively), while other nutrients had higher correlations with spectral bands or vegetation indices, mainly Ca which had coefficients higher than 0.7 with all indices derived from the spectral bands of red, red edge and near infrared. Empirical models for nutrient estimation were generated from spectral bands and vegetation indices with correlations greater than 0.5. The red edge band, positioned in a spectral region sensitive to variations in vegetation, individually participated in the models to infer the concentrations of the macronutrients Mg and S, besides the micronutrients B, Cu, Fe and Mn, but all calibrated with correlation coefficients below 0,41. The near infrared band was used in the estimation of the N, P and Na contents (R2 equal to 0.25, 0.36 and 0.49, respectively). The NDVI participated in the formulation of the inference model of Ca content and resulted in the highest calibration R2 (0.61), although the validation error was high (13.56%). The choropleth maps of Ca, Mg, Cu, Fe, Mn and Zn spatial distribution had a similar configuration, indicating almost homogeneous and high concentrations of these nutrients in most of the experimental area. The Na and B contents were different in the two plots of the experimental area, while K and S had a more heterogeneous distribution. The maps of N and P reflect well the deficiency of these nutrients in the whole area, mainly in the P content. The empirical models adjusted for the estimation of most of the nutrients were consistent with the condition of excess or deficiency of nutrients in the experimental area

CARACTERIZAÇÃO ESPECTRAL DA CANA-DE-AÇÚCAR INFECTADA POR NEMATOIDES E MIGDOLUS FRYANUS POR ESPECTRORRADIOMETRIA DE CAMPO

O cultivo da cana-de-açúcar no Brasil, embora assistido por técnicas modernas de plantio, é alvo constante de parasitas do sistema radicular. Por registrar seletivamente o fluxo espectral da radiação eletromagnética refletida pela vegetação, o sensoriamento remoto tornou-se uma poderosa ferramenta na detecção das plantas infectadas por patógenos do solo. Com o objetivo de caracterizar espectralmente a cana-de-açúcar sadia e infectada por nematoides e pela larva do besouro Migdolus fryanus, foram tomadas medidas radiométricas in situ e geradas curvas hiperespectrais de plantas sadias e infectadas. Técnicas específicas de análise espectral, como a determinação da posição da borda do vermelho limítrofe (Red Edge Position Determination – REPD) e diferentes índices espectrais foram avaliados para discriminar as três ocorrências. As curvas de reflectância mostraram diferenças em magnitude principalmente nos comprimentos de onda do vermelho e infravermelho próximo e, assim como a determinação do REP e os índices de clorofila b, NDVI, MCARI e TCARI, permitiram distinguir apenas entre plantas sadias e infectadas. As razões espectrais sensíveis aos pigmentos clorofila a e carotenoides, porém, discriminaram as três ocorrências, inclusive plantas infectadas por nematoides e Migdolus fryanus. A melhor discriminação foi obtida com o índice de carotenoides, um pigmento fortemente relacionado com estresse da planta

MAPEAMENTO DE PARÂMETROS AGRONÔMICOS DO CAFEEIRO A PARTIR DE IMAGENS TOMADAS POR AERONAVE REMOTAMENTE PILOTADA

O cultivo dos grãos de café vem se mostrando cada vez mais como uma das culturas predominantes no mercado atual. Com isso, faz-se necessário compreender e monitorar as variabilidades existentes nas grandes lavouras pelo mundo. Para tanto, as técnicas de Sensoriamento Remoto direcionadas ao monitoramento das variabilidades espectrais de uma cultura torna possível mapear e estudar as variabilidades espaciais e os fatores limitantes da produção. Dentre diversas variáveis, este trabalho objetivou o mapeamento de parâmetros relacionados à produtividade, tais como o Teor de Clorofila e a Área Foliar de uma cultura cafeeira localizada nas proximidades do município de Monte Carmelo - MG. O mapeamento foi gerado a partir de modelos de regressão através da relação entre amostras obtidas in situ com o valor radiométrico de imagens tomadas por uma aeronave remotamente pilotada à 70 e 120 metros de altitude. Os resultados mostraram que para o voo de maior altitude a correlação entre as medidas de campo e a radiometria das imagens foi melhor. A precisão dos modelos estimadores apresentou melhor correlação com os índices TGI (r = 0,536 e RMSE = 16,43%) para a clorofila e NDVI (r = 0,484 e RMSE = 15,87%) para a área foliar

Quantificação do Volume de Minérios em Pilha a Partir da Integração e Comparação Topografia/ Fotogrametria

A mineração é constituída por processos, atividades e indústrias que possuem o objetivo de extrair substâncias minerais a partir de depósitos a céu aberto, minas subterrâneas, entre outros. Atualmente no Brasil, os minerais que se destacam pela extração são os metálicos, como o ferro, o manganês e a bauxita e em menor escala o ouro, o cobre e o nióbio. As extrações desses minerais são à base da indústria mineradora brasileira, sendo Minas Gerais o estado onde se encontra a maior parte delas. O cálculo do volume do minério é de grande importância para qualquer mineradora, pois a partir desse cálculo é possível decidir qual será o melhor emprego dos materiais produzidos. O minério, após o processo de extração, é depositado em pilhas para posterior beneficiamento. O objetivo deste artigo foi mensurar e comparar o cálculo do volume das pilhas a partir da integração entre técnicas topográficas e fotogramétricas, utilizando dados obtidos por medições planialtimétricas coletadas com uma estação total e por fotogrametria terrestre, onde imagens da pilha foram coletadas por câmera fotográfica não métrica de baixo custo. Em prossecução realizou-se o cálculo do erro relativo percentual, resultando em um erro de 0,184% do levantamento fotogramétrico em relação ao levantamento topográfico