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Precision agriculture techniques contribute to optimizing the use of agricultural inputs, as they consider the spatial and temporal variability in the production factors. Prescription maps of limestone and fertilizers at variable rates (VRA) can be generated using various soil sampling techniques, such as point grid sampling, cell sampling, and management zone sampling. However, low-density grid sampling often fails to capture the spatial variability in soil properties, leading to inaccurate fertilizer recommendations. Sampling techniques by cells or management zones can generate maps of better quality and at lower costs than the sampling system by degree of points with low sampling density. Thus, this study aimed to compare the accuracy of different sampling techniques for mapping soil attributes in precision agriculture. For this purpose, the following sampling techniques were used: high-density point grid sampling method, low-density point grid sampling method, cell sampling method, management zone sampling method, and conventional method (considering the mean). Six areas located in the Brazilian states of Bahia, Minas Gerais, Mato Grosso, Goias, Mato Grosso do Sul, and Sao Paulo were used. The Root-Mean-Square-Error (RMSE) method was determined for each method using cross-validation. It was concluded that the cell method generated the lowest error, followed by the high-density point grid sampling method. Management zone sampling showed a lower error compared to the low-density point grid sampling method. By comparing different sampling techniques, we demonstrate that management zone and cell grid sampling can reduce soil sampling while maintaining comparable or superior accuracy in soil attribute mapping.

The main bottleneck to accelerating the development of new sugarcane varieties with desirable traits to meet the demands of the sugar-energy sector and adaptation to climate change is the absence of high-throughput phenotyping methods for evaluating varieties in the field. Traditional methods of field phenotyping depend on trained specialists for visual evaluations that are slow, laborious, and subjective. In this study, we investigated UAV-based multispectral data and machine learning algorithms to improve efficiency in the evaluation of field phenotyping of sugarcane varieties regarding the resistance to infection by orange and brown rusts. Spectral data from five bands (Blue, Green, Red, Red-edge, and NIR) and 14 vegetation indices were tested in direct correlations with infection scores collected in the field for the two types of rust. Sugarcane varieties were classified according to their resistance to rusts using three machine learning algorithms (Random Forest, radial SVM, and KNN). Correlations between the Red band data and infection scores of the two types of rust were significant (r = 0.67) for evaluations made at 165 days after planting (DAP). Conversely, regarding the varietal classification into three resistance classes, a high level of overall (88.1%) and balanced (Resistant = 90.3, Moderately resistant = 88.6, and Susceptible = 82.1) accuracy was reached at 195 DAP with the radial SVM model. UAV-based multispectral data is able to assist in the phenotyping of new sugarcane varieties regarding the resistance to these diseases.

In experimental fields, scientists assess the resistance to orange and brown rust of sugarcane exclusively by identifying and grading infection by visual estimation on the leaves. This is time-consuming and may deliver subjective evaluations, limiting phenotyping experiments. Thus, to facilitate the leaf disease identification process, the goal of this study was to test an image analysis approach to differentiate the two types of rust on sugarcane leaves. Radial Support Vector Machine (SVM) models showed high accuracy (>0.88) in identifying the two types of rust, classifying segments of RGB images of infected leaves generated with object-based image analysis (OBIA) segmentation. This provides a basis for the development of applications that identify the two types of rust automatically through RGB images of sugarcane leaves.

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

ABSTRACT Unmanned Aerial Vehicles (UAVs) have potentially significant application in agriculture and, with the emergence of the digital farming era and Agriculture 4.0, this platform has become increasingly important. UAV imagery may improve or even replace routine data surveys, as well as optimize phytosanitary product application. High-spatial resolution imagery makes UAVs attractive for several applications where traditional satellite sensing is still unsuitable. With the significant recent development of data science techniques, UAVs have a prominent position in assisting farmers for more efficient decision-making and automating agricultural processes. Thus, this work addresses the main agricultural applications of UAVs into five major topics: topographic survey, physiological assessments, biophysical assessments, monitoring of biological targets, and spraying of phytosanitary products and application of bio inputs.

ABSTRACT: Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative.

Abstract:The objective of this work was to evaluate whether a canopy sensor is capable of estimating sugarcane response to N, as well as to propose strategies for handling the data generated by this device during the decision-making process for crop N fertilization. Four N rate-response experiments were carried out, with N rates varying from 0 to 240 kg ha-1. Two evaluations with the canopy sensor were performed when the plants reached average stalk height of 0.3 and 0.5 m. Only two experiments showed stalk yield response to N rates. The canopy sensor was able to identify the crop response to different N rates and the relationship of the nutrient with sugarcane yield. The response index values obtained from the canopy sensor readings were useful in assessing sugarcane response to the applied N rate. Canopy reflectance sensors can help to identify areas responsive to N fertilization and, therefore, improve sugarcane fertilizer management.

The increasing world population leads to the growing demand for food production without expanding cultivation areas. In this sense, precision agriculture optimizes the production and input usage by employing sensors to locally monitor plant nutrient within agricultural fields. Here, we have used an electronic tongue sensing device based on impedance spectroscopy to recognize distinct soil samples (sandy and clayey) enriched with macronutrients. The e-tongue setup consisted of an array of four sensing units formed by layer-by-layer (LbL) films deposited onto 3D-printed graphene-based interdigitated electrodes (IDEs). The IDEs were fabricated in 20 min using the fused deposition modeling process and commercial polylactic acid-based graphene filaments. The e-tongue comprised one bare and three IDEs functionalized with poly(diallyldimethylammonium chloride) solution/copper phthalocyanine-3,4′,4″,4‴-tetrasulfonic acid tetrasodium salt (PDDA/CuTsPc), PDDA/montmorillonite clay (MMt-K), and PDDA/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) LbL films. Control samples of sandy and clayey soils were enriched with different concentrations of nitrogen (N), phosphorus (P), and potassium (K) macronutrients. Sixteen soil samples were simply diluted in water and measured using electrical impedance spectroscopy, with data analyzed by principal component analysis. All soil samples were easily distinguished without pre-treatment, indicating the suitability of 3D-printed electrodes in e-tongue analysis to distinguish the chemical fertility of soil samples. Our results encourage further investigations into the development of new tools to support precision agriculture.

O objetivo deste trabalho foi avaliar o potencial de um sensor óptico ativo terrestre como auxiliar na recomendação da aplicação de nitrogênio em taxa variável, na cultura da cana-de-açúcar. Foram instalados experimentos em delineamento de blocos ao acaso, com uso de diferentes doses de N (0, 50, 100, 150 e 200 kg ha-1). A resposta da cana-de-açúcar ao N foi avaliada por diferentes métodos - sensor óptico, clorofilômetro e teor foliar de N -, quando a altura média dos colmos atingiu 0,2, 0,4 e 0,6 m. Observou-se baixa correlação entre o teor foliar de N e a quantidade de clorofila nas folhas mensuradas por clorofilômetro. Portanto, essas características foram insuficientes para avaliar a eficiência do sensor óptico, uma vez que os valores mensurados se elevaram conforme o aumento da dose de N. A estratégia de recomendação com base na resposta da cultura, estimada pelo sensor óptico em faixa de cana-de-açúcar que recebeu a dose adequada de N, mostrou-se mais condizente com a produtividade obtida. O sensor óptico é ferramenta útil para auxiliar na recomendação de N para a cultura da cana-de-açúcar, ao se considerar a variabilidade espacial da sua demanda.

ABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs.