A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery

Abstract

Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R2 and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R2: 0.70; 12.42%), whereas the other VIs showed R2 and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs.

Analysis of the Potential of Remote Piloted Aircraft in the Detection of Damage by Frost in Coffee Plants

“…RPA has been successfully used to evaluate different conditions of coffee plants, such as nitrogen content [8,9], disease [10], biophysical parameters [11], planting errors [12] and fruit detection and maturation [13,14]. However, assessing frost damage, there is still a considerable gap to be filled.…”

Application of RGB Images Obtained by UAV in Coffee Farming

“…This alternation of coffee productivity and leaf area, due to the effects of harvesting and the incidence of diseases in consecutive years, causes changes in the spectral responses of the crop in that period [6], which are present in the same crop or even in the same plant [7]. It is expected that this variability in the coffee crop during its production cycle can be identified through patterns in the spectral responses of the plant through vegetation indices (VIs) [6].…”

“…It is expected that this variability in the coffee crop during its production cycle can be identified through patterns in the spectral responses of the plant through vegetation indices (VIs) [6]. VI are commonly used in agricultural monitoring due to their power to highlight the intrinsic characteristics of the vegetation, which are related to the reflection of green by the plants, reflecting their vigor status [7].…”