Evaluation of Coffee Plants Transplanted to an Area with Surface and Deep Liming Based on Multispectral Indices Acquired Using Unmanned Aerial Vehicles
Rafael Alexandre Pena Barata,
Gabriel Araújo e Silva Ferraz,
Nicole Lopes Bento,
Daniel Veiga Soares,
Lucas Santos Santana,
Diego Bedin Marin,
Drucylla Guerra Mattos,
Felipe Schwerz,
Giuseppe Rossi,
Leonardo Conti,
Gianluca Bambi
Affiliations
Rafael Alexandre Pena Barata
Agricultural Engineering Department, Federal University of Lavras, Lavras 37203-202, Brazil
Gabriel Araújo e Silva Ferraz
Agricultural Engineering Department, Federal University of Lavras, Lavras 37203-202, Brazil
Nicole Lopes Bento
Agricultural Engineering Department, Federal University of Lavras, Lavras 37203-202, Brazil
Daniel Veiga Soares
Agricultural Engineering Department, Federal University of Lavras, Lavras 37203-202, Brazil
Lucas Santos Santana
Agricultural Engineering Department, Federal University of Lavras, Lavras 37203-202, Brazil
Diego Bedin Marin
Agricultural Research Company of Minas Gerais (EPAMIG), Viçosa 36571-000, Brazil
Drucylla Guerra Mattos
Agricultural Department, Federal University of Lavras, Lavras 37203-202, Brazil
Felipe Schwerz
Agricultural Engineering Department, Federal University of Lavras, Lavras 37203-202, 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
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.
