UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers

Rafael Alexandre Pena Barata, Gabriel Araújo e Silva Ferraz (), Nicole Lopes Bento, Lucas Santos Santana, Diego Bedin Marin, Drucylla Guerra Mattos, Felipe Schwerz, Giuseppe Rossi, Leonardo Conti and Gianluca Bambi
Additional contact information
Rafael Alexandre Pena Barata: Agricultural Engineering Department, School of Engineering, Federal University of Lavras, Lavras 37200-900, Brazil
Gabriel Araújo e Silva Ferraz: Agricultural Engineering Department, School of Engineering, Federal University of Lavras, Lavras 37200-900, Brazil
Nicole Lopes Bento: Agricultural Engineering Department, School of Engineering, Federal University of Lavras, Lavras 37200-900, Brazil
Lucas Santos Santana: Agricultural Engineering Department, School of Engineering, Federal University of Lavras, Lavras 37200-900, Brazil
Diego Bedin Marin: Agricultural Research Company of Minas Gerais (EPAMIG), Viçosa 36571-000, Brazil
Drucylla Guerra Mattos: Department of Agriculture, School of Agriculture, Federal University of Lavras, Lavras 37200-900, Brazil
Felipe Schwerz: Agricultural Engineering Department, School of Engineering, Federal University of Lavras, Lavras 37200-900, Brazil
Giuseppe Rossi: Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy
Leonardo Conti: Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy
Gianluca Bambi: Department of Agriculture, Food, Environment and Forestry, University of Florence, 50145 Florence, Italy

Agriculture, 2024, vol. 14, issue 3, 1-20

Abstract: 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 (cm 2 ), 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.

Keywords: remote sensing; sustainable crop production; coffee monitoring; seedling container; UAV (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/3/356/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/3/356/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:3:p:356-:d:1344473

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().