Precision Agriculture (PA) technologies introduction in coffee-growing is becoming essential to advances in sustainable cultivation and increase in output. Applications that involve PA techniques in coffee production are defined now as Precision Coffee growing (PC). Systematically explored, studies on the subject contribute to improvements in the area, relating soil variability to its impacts on plants. The PC's scientific approach offers new forms of management and more security in coffee production. Aimed at reducing pesticides application and nutrients to the soil, contributing to sustainable development in coffee production. Initially, the research on coffee production had dealt with soil spatial variability, highlighting the geostatistical methods and specific ways to sample the soil. With technological advances in agriculture, new ways of monitoring spatial variability are available. In this context, studies are arising on spatial variability related to the plant, applying terrestrial, aerial and orbital sensors, possibly creating perspectives for monitoring and mapping coffee production. Artificial intelligence, Remotely Piloted Aircraft (ARP) products, harvesting yield sensors, automatic grain classifiers, and remote sensing stand out as new technologies under development in coffee production. These applications in PC involving multidisciplinary research demonstrate new relevant ways of improving crop managing and sustainability guaranteeing. Key words: Digital agriculture; spatial variability; sustainability of cultivation; remote sensing; Sensors.

The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants.

A tool that has been widely used in Precision Agriculture (PA) is the Remote Piloted Aircraft’s (RPA’s). These tools are used to monitor crops, in addition to checking and quantifying various attributes related to plants. However, there are few studies that evaluate the applicability of this technology in coffee plantations. The objective of this study is to present the applicability of two tools associated with PA and remote sensing to monitoring a coffee plantation. The study was conducted in the municipality of Três Pontas, Brazil, comprised a 1.2 ha coffee plantation. Data were collected during a flight with an eBee SQ RPA, and high spatial resolution images were captured by a Parrot Sequoia multispectral sensor coupled to the aircraft. The images were processed using the software Pix4D, thus creating an orthomosaic that was later uploaded to QGIS software. In this program, a supervised classification of land use and land cover was performed using the maximum likelihood method, and the following classes were obtained: coffee plant, exposed soil, and undergrowth. From the mapping accuracy, an overall accuracy and kappa index of 91% and 85% were obtained, respectively. In addition to the supervised classification of the site, the normalized difference vegetation index (NDVI) was calculated for only the coffee plant class. The NDVI map showed the areas of the plantation coffee crop with higher and lower vegetative vigour. Key words: Mapping; precision coffee farming; remotely piloted aircraft.

This study aimed to characterize the spatial variability of pH in soils of two farms in the state of Paraná, Brazil, based on two different sampling methods used in precision agriculture, by means of geostatistical analyzes. The first method of sampling the pH grid consisted in the collection of soil samples by the traditional method (1 point / ha). The second method of pH determination was by on-the-go soil sensor (200 points / ha). The spherical model was better suited to most semivariograms, regardless of the sampling method. After adjusting the semivariograms for soil pH determination methods, thematic maps were made using normal kriging. The best spatial distribution of pH was obtained where the attribute was sampled by the on-the-go sensor. The number of pH samples collected and the sampling method influenced the visual representation of pH variability.

The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study evaluated the performance of different ML algorithms in predicting Arabica coffee (Coffea arabica) yield using field-based biophysical measurements and spectral variables extracted from multispectral UAV imagery. The research was conducted over two crop seasons (2020/2021 and 2021/2022) in a 1.2-hectare experimental plot in southeastern Brazil. Three modeling scenarios were tested with Random Forest, Gradient Boosting, K-Nearest Neighbors, Multilayer Perceptron, and Decision Tree algorithms, using Leave-One-Out cross-validation. Results varied considerably across seasons and scenarios. KNN performed best with raw data, while Gradient Boosting was more stable after variable selection and synthetic data augmentation with SMOTE. Nevertheless, limitations such as small sample size, seasonal variability, and overfitting, particularly with synthetic data, affected overall performance. Despite these challenges, this study demonstrates that integrating UAV-derived spectral data with ML can support yield estimation, especially when variable selection and phenological context are carefully addressed.

The application of precision agriculture in cocoa and papaya cultivation in Brazil is still incipient. This study aimed to evaluate the spatial variability of the physical attributes of soil cultivated with a consortium of papaya and cocoa. The study was conducted in two sampling grids of 50 points, in two areas cultivated with papaya and cocoa with different planting times (three and eleven months). The soil attributes soil resistance to penetration (RP) and soil gravimetric moisture (UG) were determined at soil depths of 0–20, 20–40 and 40–60 cm. The data were submitted to an exploratory and descriptive analysis. Subsequently, a geostatistical analysis was performed to quantify spatial dependence and then interpolation of the data through kriging. The maps showed weak spatial variability for the UG and RP. In the two areas, it was observed that the depth of 0–20 cm had a lower RP (1.7 Mpa) and a higher UG (40 g g⁻¹), and as the depth was higher, had a higher RP (4.4 Mpa) and a lower UG (38 g g⁻¹). Area 1 presented higher RP values in depth, showing greater susceptibility to compaction. The area characterized by the consortium of papaya and cocoa presented more susceptible to compaction. The mechanical resistance of the soil to penetration was more critical in the 40–60 cm layer for the two consortia evaluated, evidencing areas with possible restriction to plant growth.