Unmanned aerial vehicle to evaluate frost damage in coffee plants

• Published in: Precision agriculture 2021-12, Vol.22 (6), p.1845-1860
• Main Authors: Marin, Diego Bedin, Ferraz, Gabriel Araújo e Silva, Schwerz, Felipe, Barata, Rafael Alexandre Pena, de Oliveira Faria, Rafael, Dias, Jessica Ellen Lima
• Format: Article
• Language: English
• Subjects: Agriculture; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Chlorophyll; Coffee; Computer Science; Crop management; Damage assessment; Damage detection; Decision making; Frost; Frost damage; Identification methods; Impact damage; Life Sciences; Normalized difference vegetative index; Physics; Remote sensing; Remote Sensing/Photogrammetry; Soil Science & Conservation; Statistics for Engineering; Unmanned aerial vehicles; Vegetation; Vegetation index
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-021-09815-w

Damage caused by frost on coffee plants can impact significantly in the reduction of crop quality and productivity. Remote sensing can be used to evaluate the damage caused by frost, providing precise and timely agricultural information to producers, assisting in decision making, and consequently minimizing production losses. In this context, this study aimed to evaluate the potential use of multispectral images obtained by unmanned aerial vehicle (UAV) to analyze and identify damage caused by frost in coffee plants in different climatic favorability zones. Visual evaluations of frost damage and chlorophyll content quantification were carried out in a commercial coffee plantation in Southern Minas Gerais, Brazil. The images were obtained from a multispectral camera coupled to a UAV with rotating wings. The results obtained demonstrated that the vegetation indices had a strong relationship and high accuracy with the frost damage. Among the indices studied the normalized difference vegetation index (NDVI) was the one that had better performances (r = − 0.89, R 2  = 0.79, MAE = 10.87 e RMSE = 14.35). In a simple way, this study demonstrated that multispectral images, obtained from UAV, can provide a fast, continuous, and accessible method to identify and evaluate frost damage in coffee plants. This information is essential for the coffee producer for decision-making and adequate crop management.