Laila Luana Campos
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Laila Luana Campos has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Amara Lana Valim Abreu
Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), s/n, P.O. Box 3037, Lavras 37200-900, Brazil
1 shared publication - Gabriel Araújo e Silva Ferraz
Agricultural Engineering Department, Federal University of Lavras, Lavras 37200-000, Brazil
1 shared publication - Giuseppe Rossi
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
1 shared publication - Matteo Barbari
Department of Agriculture, Food, Environment and Forestry, University of Firenze, 50145 Firenze, Italy
1 shared publication - Rafael de Oliveira Faria
Agricultural Engineering Department, Federal University of Lavras, Lavras 37200-000, Brazil
1 shared publication - Rosalra Maria Alves de Morais
Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), s/n, P.O. Box 3037, Lavras 37200-900, Brazil
1 shared publication
Latest Publications
- Open Access
- 23 July 2026
- Published by MDPI AG in Horticulturae
- Vol. 12 (8) , 904
Mapping Frost Damage in Coffee Plantations Using RPA (Remotely Piloted Aircraft)-Mounted LiDAR (Light Detection and Ranging) Structural Metrics
Precision agriculture (PA) relies on data collection and analysis to understand spatial variability in agricultural crops, using technologies such as sensors, GNSS, remotely piloted aircraft (RPAs), and management software. This study aimed to evaluate the potential of a LiDAR sensor coupled to an RPA to map damage in coffee plants after frosts and fill scientific gaps. To this end, the experiment was conducted in Santo Antônio do Amparo, Brazil, in May 2023, obtaining data on height and canopy diameter of three coffee plantations, aged 3, 4, and 10 years, affected by frosts in different years (2019 and 2021). Multivariate statistical analyses were performed using non-hierarchical clustering tools to classify the coffee plants into two groups according to frost incidence. Two new classifications were proposed for the two groups, based on k-means non-hierarchical cluster analysis. Thus, the study showed significant differences in the variables, with a p-value of −16 for the proposed groupings 1 and 2. Therefore, the groups are statistically different.
- 23 July 2026
- Published by MDPI AG in Horticulturae
- Vol. 12 (8) , 904
Open AccessMapping Frost Damage in Coffee Plantations Using RPA (Remotely Piloted Aircraft)-Mounted LiDAR (Light Detection and Ranging) Structural Metrics
Precision agriculture (PA) relies on data collection and analysis to understand spatial variability in agricultural crops, using technologies such as sensors, GNSS, remotely piloted aircraft (RPAs), and management software. This study aimed to evaluate the potential of a LiDAR sensor coupled to an RPA to map damage in coffee plants after frosts and fill scientific gaps. To this end, the experiment was conducted in Santo Antônio do Amparo, Brazil, in May 2023, obtaining data on height and canopy diameter of three coffee plantations, aged 3, 4, and 10 years, affected by frosts in different years (2019 and 2021). Multivariate statistical analyses were performed using non-hierarchical clustering tools to classify the coffee plants into two groups according to frost incidence. Two new classifications were proposed for the two groups, based on k-means non-hierarchical cluster analysis. Thus, the study showed significant differences in the variables, with a p-value of −16 for the proposed groupings 1 and 2. Therefore, the groups are statistically different.