Precision phenotyping in peanut culture to detect water deficit

Abstract

Peanuts are an oilseed known to contain the potential to be cultivated in areas with less water availability, however when subjected to the condition of stress to water deficit, it causes changes in the physiological and biochemical system, such as stoma closure, which reduces sweating and, consequently, supply of CO2 for photosynthesis, which, depending on the management conditions, these damages may be irreversible. Thus, the demand for cultivars with better overall agronomic performance in regions with greater water limitation is increasing, leading breeders to expand their experiments looking for methodologies that reduce the time between research and the introduction of their results in the market. One of the alternatives for large-scale, high-quality experimental optimization is phenotyping by means of images. This technique approached by phenomics efficiently evaluates high-quality digital images from field experiments through Unmanned Aerial Vehicles (UAVs), obtaining data through digital analyzes correlated to digital image processing libraries. When obtaining these data, the measurement of phenotyping becomes more accurate in relation to the descriptors with manual measurements. The objective of this work is to evaluate the efficiency of phenotyping in the cultivation of peanuts through digital images, ensuring precision and practicality for future research.