The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer.

Brazil is the main producer and exporter and the second-largest consumer of coffee in the world, and Remotely Piloted Aircraft Systems stands out as an efficient remote detection technique applied to the study and mapping of crops. The objective of this study was to characterize three recently planted cultivars of Coffea arabica L. The study area is in Minas Gerais, Brazil, with a coffee plantation of the initial age of 5 months. The temporal behavior was determined based on monthly mean values. The spectral profile was obtained with mean values of the last month of dry and rainy periods. The statistical differences were obtained based on the non-parametric test of multiple comparisons. The estimation of the exponential equation was obtained through the Spearman correlation coefficient of determination and root mean square error. It was concluded that the seasons influence the behavior and development of cultivars, and significant statistical differences were detected for the variables, except for the chlorophyll variable. Due to the proximity and overlap of the reflectance values, spectral bands were not used to individualize cultivars. A correlation between the vegetation indices and leaf area index was observed and the exponential regression equation was estimated for each cultivar under study.

The differentiation between the main crop and weeds is an important step for selective spraying systems to avoid agrochemical waste and reduce economic and environmental impacts. In this sense, this study aims to classify and map the area occupied by weeds, determine the percentage of area occupied, and indicate treatment control strategies to be adopted in the field. This study was conducted by using a yellow Bourbon cultivar (IAC J10) with 1 year of implementation on a commercial coffee plantation located at Minas Gerais, Brazil. The aerial images were obtained by a remotely piloted aircraft (RPA) with an embedded multispectral sensor. Image processing was performed using PIX4D, and data analysis was performed using R and QGIS. The random forest (RF) and support vector machine (SVM) algorithms were used for the classification of the regions of interest: coffee, weed, brachiaria, and exposed soil. The differentiation between the study classes was possible due to the spectral differences between the targets, with better classification performance using the RF algorithm. The savings gained by only treating areas with the presence of weeds compared with treating the total study area are approximately 92.68%.

Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm²), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach.

Digital agriculture is fundamental to potential improvements in the field by optimizing processes and providing intelligent decision making. This study aims to calculate the height and canopy diameter of recently transplanted coffee plants over three periods of crop development using aerial images, verify statistical differences between field measurements and aerial images, estimate linear equations between field data and aerial images, and monitor the temporal profile of the growth and development of the cultivar understudy in the field based on information extracted from aerial images through a Remotely Piloted Aircraft System (RPAS). The study area comprises a recently transplanted five-month-old Coffea arabica L. cultivar IAC J10 with information of height and crown diameter collected in the field and aerial images obtained by RPAS. As a result, it was possible to calculate the height and diameter of the canopy of coffee plants by aerial images obtained by RPAS. The linear estimation equation for height and crown diameter was determined with satisfactory results by coefficients R and R² and performance metrics MAE, RMSE, and regression residuals, and it was possible to monitor the temporal profile of the height of the coffee cultivar in the field based on aerial images.