Uso de sensores LiDAR em RPA no monitoramento de cafeeiros atingidos por geada

Abstract

Precision Agriculture (PA) is based on data collection and analysis to understand spatial variability within the workforce, allowing agricultural production to optimize the use of inputs such as fertilizers, pesticides and water. Through technologies such as sensors, GNSS, remotely piloted aircraft (RPAs) and management software, PA assists in controlled interventions, increasing production efficiency, reducing costs and minimizing environmental impacts. This study aims to evaluate the potential of a LiDAR sensor embedded in an RPA to map damage to coffee plants after incidents. To this end, the experiment was conducted in Santo Antonio do Amparo, in 3 coffee activities, with planting ages of 3, 4 and 10 years. Coffee crops were affected in different years by the Pacific (2019 and 2021) and the experiment to obtain the information was completed in May 2023. After obtaining the data on the plant population (plant height and canopy diameter), analyses were performed with tools using multivariate hierarchical clustering statistics to classify the coffee plants into 2 groups, according to the incidence of incidence, being: 1 plants with the highest incidence and 2 with the lowest incidence. In order to compare the means of the variables between groups 1 and 2, an analysis of variance was performed. Two new classifications were also proposed for groups 1 and 2, based on the analysis of non-hierarchical k-means clustering. For this, the variables of height and canopy diameter were used in one grouping, and in another, the variables of height, canopy diameter and altimetry were considered, for the 3 areas under study. Thus, the present study made it possible to develop and test a methodology for mapping occurrence zones through groupings based on data obtained by LiDAR, analyzing whether the groups affected after the occurrence of occurrences found differences in the variations demonstrated in plant height, canopy diameter, plant volume and IAF, thus obtaining impressive results in the study.