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Photogrammetric data are systematically used in several segments. Products such as Digital Terrain Models (DTMs) provide detailed surface information, however the geometric reliability of these products is questionable compared to data collected by topographic survey by GNSS RTK. The present research assesses the quality of DTMs obtained using an Unmanned Aerial Vehicle (UAV) with different parameters, overlap percentages, and flight directions, comparing the results to those of the topography method Global Navigation Satellite System - Real-Time Kinematic (GNSS RTK). Were done twelve flight plans with different overlaps (90x90, 80x80, 80x60, 70x50, 70x30, and 60x40%) and directions (transverse and longitudinal to the planting line). The parameters of height (Above Ground Land- AGL) and speed were fixed at 90 m and 3 m/s respectively and a Ground Sample Distance (GSD) of 0,1 m is obtained for all flights. Overall, the flight with 70x50% overlap in the transverse direction generated the best results, with a total processing time of 12 minutes and 17 seconds (about 1.5 hours faster than 90x90%), an Root Mean Square Error (RMSE) 0.589 m, and meets the minimum overlap required by 60X30% aerophotogrammetry; furthermore, the results did not differ statistically from the high overlaps of 90x90% and 80x80%.

Considered a biostimulant, chitosan can affect the physiological responses of plants to water deficit, acting as an antitranspirant under agricultural stress. Currently, images obtained by Remotely Piloted Aircraft Systems (RPAS), together with machine learning techniques, aid in resolving agricultural problems, including water issues. Therefore, the objective of this study was to differentiate between coffee plants subjected to the foliar application of chitosan and those not subjected to it, based on spectral data extracted from RPAS-acquired images and classification via machine learning. For this purpose, the random forest (RF) classifier was applied to two coffee cultivars (Catucaí Amarelo 2SL and Catuaí Vermelho IAC 99) over two years of study (2021 and 2022). The images were obtained by a 3DR SOLO aircraft with a Parrot Sequoia sensor, processed in PIX4D Mapper software and analysed in QGIS and RStudio software. The results showed good performance metrics for differentiating between coffee plants subjected and not subjected to the foliar application of chitosan, indicating that this method is a valid approach for modelling the presence of the biostimulant in coffee plants, thus confirming that the model can efficiently support the practices of precision agriculture.

Remotely Piloted Aircraft (RPA) as sensor-carrying airborne platforms for indirect measurement of plant physical parameters has been discussed in the scientific community. The utilization of RGB sensors with photogrammetric data processing based on Structure-from-Motion (SfM) and Light Detection and Ranging (LiDAR) sensors for point cloud construction are applicable in this context and can yield high-quality results. In this sense, this study aimed to compare coffee plant height data obtained from RGB/SfM and LiDAR point clouds and to estimate soil compaction through penetration resistance in a coffee plantation located in Minas Gerais, Brazil. A Matrice 300 RTK RPA equipped with a Zenmuse L1 sensor was used, with RGB data processed in PIX4D software (version 4.5.6) and LiDAR data in DJI Terra software (version V4.4.6). Canopy Height Model (CHM) analysis and cross-sectional profile, together with correlation and statistical difference studies between the height data from the two sensors, were conducted to evaluate the RGB sensor’s capability to estimate coffee plant height compared to LiDAR data considered as reference. Based on the height data obtained by the two sensors, soil compaction in the coffee plantation was estimated through soil penetration resistance. The results demonstrated that both sensors provided dense point clouds from which plant height (R2 = 0.72, R = 0.85, and RMSE = 0.44) and soil penetration resistance (R2 = 0.87, R = 0.8346, and RMSE = 0.14 m) were accurately estimated, with no statistically significant differences determined between the analyzed sensor data. It is concluded, therefore, that the use of remote sensing technologies can be employed for accurate estimation of coffee plantation heights and soil compaction, emphasizing a potential pathway for reducing laborious manual field measurements.

Brazil is the largest producer and exporter of coffee beans in the world. Given this relevance, it is important to monitor the crop to prevent attacks by pests. This study aimed to detect leaf miner (Leucoptera coffeella) infestation in a newly planted crop based on vegetation indices (VI) derived from aerial images obtained by a multispectral camera embedded in a remotely piloted aircraft (RPA) using random forest (RF). The study was conducted on the Cafua farm in the municipality of Lavras in southern Minas Gerais. The images were collected using a multispectral camera attached to a remotely piloted aircraft (RPA). Collections were carried out on 30 July 2019 (infested crop) and 16 December 2019 (post chemical control). The RF package in R software was used to classify the infested and healthy plants. The t test revealed significant differences in band means between healthy and infested plants, favouring higher means in healthy plants. VI also exhibited significant differences, with EXR being higher in infested plants and GNDVI, GOSAVI, GRRI, MPRI, NDI, NDRE, NDVI and SAVI showing higher averages in healthy plants, indicating distinct spectral responses and light absorption patterns between the two states of the plant. Due to the spectral differences between the classes, it was possible to classify the infested and healthy plants, and the RF algorithm performed very well.

Residues mapping can provide essential information about soil chemical elements’ behaviors and contribute to possible interferences in coffee tree development. Thus, the research objective was to monitor plant residue burning effects by analyzing the chemical elements in ash, using soil analysis, and applying vegetative indices obtained by RPA images. The samples were submitted for conventional soil analysis and atomic emission spectrometry (pure ash). The RPA multispectral images were used to form thirty-one vegetative indices. Thus, at the soil and ash collection points, the index performance was evaluated for six months and divided into three collection times. Then, the data were statistically analyzed to evaluate which index best separated the plants in regions with ash and ash-free soil. The pure ash deposits revealed expressive presences of K, Ca, Mg, and Al in addition to pH elevation. In areas with ash, the high temperature at the burning time may have caused elemental chemical transformations in the Al composition, making this element unavailable in soil analysis. The vegetative indices showed a significant difference only in coffee four months after planting. Among the thirty-one evaluated indices, only twenty were satisfactory for ash analysis. The burning of plant residues promoted the neutralization of Al. In addition, ash deposits in the soil added some essential elements for plant development. Negatively, they raised the PH and made micronutrients unavailable. The best vegetative indices for ash monitoring were the Normalized Near Infrared Index (NNIRI) and Normalized Green Index (NGI). Prior ash mapping can contribute to localized application in macro, such as K and limestone, reusing the number of elements already deposited by burning vegetables.

Computer vision algorithms for counting plants are an indispensable alternative in managing coffee growing. This research aimed to develop an algorithm for automatic counting of coffee plants and to determine the best age to carry out monitoring of plants using remotely piloted aircraft (RPA) images. This algorithm was based on a convolutional neural network (CNN) system and Open Source Computer Vision Library (OpenCV). The analyses were carried out in coffee-growing areas at the development stages three, six, and twelve months after planting. After obtaining images, the dataset was organized and inserted into a You Only Look Once (YOLOv3) neural network. The training stage was undertaken using 7458 plants aged three, six, and twelve months, reaching stability in the iterations between 3000 and 4000 it. Plant detection within twelve months was not possible due to crown unification. A counting accuracy of 86.5% was achieved with plants at three months of development. The plants’ characteristics at this age may have influenced the reduction in accuracy, and the low uniformity of the canopy may have made it challenging for the neural network to define a pattern. In plantations with six months of development, 96.8% accuracy was obtained for counting plants automatically. This analysis enables the development of an algorithm for automated counting of coffee plants using RGB images obtained by remotely piloted aircraft and machine learning applications.

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.

The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers.